# Journal of Intelligent and Fuzzy Systems

Online ISSN: 1064-1246
Publications
Conference Paper
Many safety (risk) analyses depend on uncertain inputs and on mathematical models chosen from various alternatives, but give fixed results (implying no uncertainty). Conventional uncertainty analyses help, but are also based on assumptions and models, the accuracy of which may be difficult to assure. Some of the models and assumptions that on cursory examination seem reasonable can be misleading. As a result, quantitative assessments, even those accompanied by uncertainty measures, can give unwarranted impressions of accuracy. Since analysis results can be a major contributor to a safety-measure decision process, risk management depends on relating uncertainty to only the information available. The uncertainties due to abnormal environments are even more challenging than those in normal-environment safety assessments; and therefore require an even more cautious approach. A fuzzy algebra analysis is proposed in this paper that has the potential to appropriately reflect the information available and portray uncertainties well, especially for abnormal environments

Conference Paper
Tuning and controlling particle accelerators is time consuming and expensive. Inherently nonlinear, the control problem is one to which conventional methods cannot satisfactorily be applied. Advanced information technologies such as expert systems and neural networks have been applied separately to the problem, with isolated success. Few, if any, of these advanced information technologies have been applied for general use or in a manner useful to multiple accelerator installations. We discuss results of coupling neural network and expert systems technology to solve several standard accelerator tuning problems based on realistic simulations. We also examine the effectiveness of additional heuristic search techniques such as genetic algorithms. Finally, we show the integration of this hybrid AI system with an existing general-purpose control system

Conference Paper
In this paper, we study connection-oriented service in heterogeneous network for real-time appli cations. Many existing distributed mission-critical systems are deployed over heterogeneous networks. Hence, it is necessary to extend the real-time communication technology to encompass heterogeneous networks. A connection can be considered as a contract between an application and the network: the application specifies the characteristics of the traffic which it may generate and the network agrees to provide the requested quality of service (QoS) to the application. For real-time applications, the most crucial QoS is to meet deadline requirements.

Conference Paper
A four link biomechanical model of human stable movement is studied through fuzzy modeling and optimal control. Physiologists describe human sit-to-stand movement in different phases; a local linear model is developed for each phase and integrating all local models with Gaussian membership functions. These local linear models of sit-to-stand movement are generated from the general nonlinear model. The knee flexion during sit to stand movement provides the criterion for determining the weights of fuzzy membership functions. The torque inputs for the fuzzy model are computed by using optimal control techniques. In this paper we study a linear fuzzy model based H<sub>2</sub> controller for linear and nonlinear plant. Linear fuzzy Kalman filter is designed for estimating the states. The measurement noise in knee flexion and input noise in the plant model are analyzed through (linear quadratic Gaussian) LQG methods to determine the control inputs for the biomechanical model

Conference Paper
In this paper, two kinds of fuzzy Choquet integral are first defined: one is the fuzzy Choquet integral of fuzzy-valued functions with respect to fuzzy measure, the another is the fuzzy Choquet integral of real-valued functions with respect to fuzzy-valued fuzzy measure. Using a nonadditive crisp/fuzzy set function to describe the interaction among attributes, two kinds of new nonlinear fuzzy multiregression are established based on fuzzy Choquet integrals. Such models are generalizations of the traditional fuzzy linear multiregression.

Conference Paper
In communications, often, statistical characteristics of the noise vary so much that, in essence, we can only have an interval estimates for this noise. We show that under this interval uncertainty, the optimal multiplexing technique consists of using Walsh functions. This result provides a new theoretical explanation for the success of Walsh-based multiplexing techniques which are actively used in cellular phones and in other types of wireless communication

Conference Paper
Theoretical studies have shown that fuzzy models are capable of approximating any continuous function on a compact domain to any degree of accuracy. However, good performance in approximation does not necessarily assure good performance in prediction or control. A fuzzy model with a large number of fuzzy rules may have a low accuracy of estimation for the unknown parameters. This is especially true when only limited sample data are available in building the model. Further, such a model often encounters the risk of overfitting the data and thus has a poor ability of generalization. A trade-off is thus required in building a fuzzy model: on the one hand, the number of fuzzy rules must be sufficient to provide the discriminating capability required for the given application; on the other hand, the number of fuzzy rules must be "parsimonious" to guarantee a reasonable accuracy of parameter estimation and a good ability of generalizing to unknown patterns. In this paper we apply statistical information criteria for achieving such a trade-off. In particular, we combine these criteria with an SVD (singular value decomposition) based fuzzy rule selection method to choose the optimal number of fuzzy rules and construct the "best" fuzzy model. The role of these criteria in fuzzy modeling is discussed and their practical applicability is illustrated using a nonlinear system modeling example.

Conference Paper
The kinematic redundancy enables a robot to change its joint configuration without changing the pose of the end-effector or the object. In addition to the requirement of path tracking in Cartesian space, many tasks require a manipulator to achieve more than one performance criteria or take into account some manipulation constraints. In this paper, a fuzzy logic approach is proposed for the desired joint path generation for redundant manipulators with consideration of multiple criteria. Performance criteria are fuzzified and their relative importance measures are introduced. The desired joint path is determined based on the relative importance of various criteria and the satisfaction measures of each candidate joint angle to all criteria by a fuzzy logic multiple criteria decision making process. Simulation results are presented to demonstrate that the proposed method can generate a path which can satisfy two performance criteria

Conference Paper
Nonsingleton fuzzy logic systems (NSFLSs) are generalizations of singleton fuzzy logic systems (FLSs), that are capable of handling set-valued input. In this paper, we extend the theory of NSFLSs by presenting an algorithm to design and train such systems. Since they generalize singleton FLSs, the algorithm is equally applicable to both types of systems. The proposed SVD-QR method selects subsets of independent basis functions which are sufficient to represent a given system, through operations on a nonsingleton fuzzy basis function matrix. In addition, it provides an estimate of the number of necessary basis functions. We present examples to illustrate the ability of the SVD-QR method to operate in uncertain environments

Conference Paper
This paper addresses the robust H2 control design of complex nonlinear systems which can be represented by a fuzzy dynamic model. Based on a continuous Lyapunov function and a piecewise continuous Lyapunov function respectively, two kinds of new control design methods are proposed using Lyapunov stability theory, robust H2 control theory and Linear Matrix Inequalities (LMI) techniques. It is shown that the closed loop fuzzy control system is asymptotically stable with a white noise attenuation. The robust H2 fuzzy controller can be obtained by using the LMI techniques. An example is given to demonstrate the application of the proposed design methods.

Conference Paper
PL classical task in the protection of transmission lines against short circuits is the estimation of the electrical distance to the fault and its comparison against a given threshold to determine whether the line is faulted or not. This paper presents a novel neural network approach to this problem, as a step towards the design of a neural protective relay. Two different alternatives are proposed and evaluated in the paper, which resemble the amplitude and phase signal comparison principles currently used in analog protective relays. Obtained results confirm that neural network technology can successfully be used to estimate the fault location for transmission line protection.

Conference Paper
A learning controller that is developed by synthesizing several basic ideas from fuzzy set and control theory, self-organizing control, and conventional adaptive control is introduced. A learning mechanism that observes the plant outputs and adjusts the rules in a direct fuzzy controller so that the overall system behaves like a reference model is used. The effectiveness of this fuzzy model reference learning controller is evaluated by comparing its performance to that of a self-organizing controller for a cart and pendulum system

Conference Paper
This paper presents a speed control system for ultrasonic motors by PI (proportional and integral) control which has auto-tuning structure based reasoning realized by a neural network. The speed characteristics of the ultrasonic motor vary with temperature and applied load torque, therefore, adjusting the control gains of the PI controller is necessary in order to obtain fine speed control performance. The proposed control scheme can incorporate the operators's knowledge in a speed controller using fuzzy reasoning, furthermore, it can compensate the speed characteristic variations of the motor by online learning. The usefulness and validity of the proposed control scheme is examined by experiments

Conference Paper
Individuals who experience random motor spasms during forearm motion were selected from diagnostic groups including head trauma, cerebro-vascular accident, and cerebral palsy. An engineering analysis technique involving pursuit target tracking examination in the error phase plane and fuzzy logic is applied to the problem of recognition of an uncommanded motion of the forearm. These patterns are then used as the basis for a proposed adaptive controller that would assist the individual in overcoming the motor spasm and returning to useful control

Article
We characterize intra-regular Abel-Grassmann's groupoids by the properties of their ideals and $(\in ,\in!\vee q_{k})$-fuzzy ideals of various types.

Article
In this paper, we will provide an algorithm which allows us to find a BCK-algebra starting from a given block code.

Article
In this paper, using N-structure, the notion of an N-ideal in a BE-algebra is introduced. Conditions for an N-structure to be an N-ideal are provided. To obtain a more general form of an N-ideal, a point N-structure which is (k conditionally) employed in an N-structure is proposed. Using these notions, the concept of an N-ideal is introduced and related properties are investigated. N-ideal is a generalized form of N-ideal. Characterizations of N-ideals are discussed.

Article
An approach to robotics called layered evolution and merging features from the subsumption architecture into evolutionary robotics is presented, and its advantages are discussed. This approach is used to construct a layered controller for a simulated robot that learns which light source to approach in an environment with obstacles. The evolvability and performance of layered evolution on this task is compared to (standard) monolithic evolution, incremental and modularised evolution. To corroborate the hypothesis that a layered controller performs at least as well as an integrated one, the evolved layers are merged back into a single network. On the grounds of the test results, it is argued that layered evolution provides a superior approach for many tasks, and it is suggested that this approach may be the key to scaling up evolutionary robotics.

Article
In this article, we combine the concept of a bipolar fuzzy set and a soft set. We introduce the notion of bipolar fuzzy soft set and study fundamental properties. We study basic operations on bipolar fuzzy soft set. We define exdended union, intersection of two bipolar fuzzy soft set. We also give an application of bipolar fuzzy soft set into decision making problem. We give a general algorithm to solve decision making problems by using bipolar fuzzy soft set.

Article
We evaluate a version of the recently-proposed Optimized Dissimilarity Space Embedding (ODSE) classification system that operates in the input space of sequences of generic objects. The ODSE system has been originally presented as a labeled graph classification system. However, since it is founded on the dissimilarity space representation of the input data, the classifier can be easily adapted to any input domain where it is possible to define a meaningful dissimilarity measure. We demonstrate the effectiveness of the ODSE classifier for sequences considering an application dealing with recognition of the solubility degree of the Escherichia coli proteome. Overall, the obtained results, which we stress that have been achieved with no context-dependent tuning of the ODSE system, confirm the validity and generality of the ODSE-based approach for structured data classification.

Article
Maji\cite{maj-13}, firstly proposed neutrosophic soft sets can handle the indeterminate information and inconsistent information which exists commonly in belief systems. In this paper, we have firstly redefined complement, union and compared our definitions of neutrosophic soft with the definitions given by Maji. Then, we have introduced the concept of neutrosophic soft matrix and their operators which are more functional to make theoretical studies in the neutrosophic soft set theory. The matrix is useful for storing an neutrosophic soft set in computer memory which are very useful and applicable. Finally, based on some of these matrix operations a efficient methodology named as NSM-decision making has been developed to solve neutrosophic soft set based group decision making problems.

Article
In this work, we first define intuitionistic fuzzy parametrized soft sets (intuitionistic FP-soft sets) and study some of their properties. We then introduce an adjustable approaches to intuitionistic FP-soft sets based decision making. We also give an example which shows that they can be successfully applied to problems that contain uncertainties.

Article
In this paper an alternative approach to solve uncertain Stochastic Differential Equation (SDE) is proposed. This uncertainty occurs due to the involved parameters in system and these are considered as Triangular Fuzzy Numbers (TFN). Here the proposed fuzzy arithmetic in [2] is used as a tool to handle Fuzzy Stochastic Differential Equation (FSDE). In particular, a system of Ito stochastic differential equations is analysed with fuzzy parameters. Further exact and Euler Maruyama approximation methods with fuzzy values are demonstrated and solved some standard SDE.

Article
Ranking of intuitionsitic fuzzy number plays a vital role in decision making and other intuitionistic fuzzy applications. In this paper, we propose a new ranking method of intuitionistic fuzzy number based on distance measure. We first define a distance measure for interval numbers based on Lp metric and further generalize the idea for intuitionistic fuzzy number by forming interval with their respective value and ambiguity indices. Finally, some comparative results are given in tabular form.

Article
B. Tanay et. al. introduced and studied fuzzy soft topological spaces. Here we introduce fuzzy soft point and study the concept of neighborhood of a fuzzy soft point in a fuzzy soft topological space. We also study fuzzy soft closure and fuzzy soft interior. Separation axioms and connectedness are introduced and investigated for fuzzy soft topological spaces.

Article
A Globally Coupled Map (GCM) model is a network of chaotic elements that are globally coupled with each other. We have previously proposed an associative memory system based on GCM, which has a better ability than the Hopfield network. This result indicates that the dynamics of our system is more efficient than that of the Hopfield network. However, even in our system, spurious memories, i.e., system's equilibria that do not correspond to any of proper memories, do exist. In this paper, we propose a modified associative memory system, in which spurious memories are noticeably reduced. This is achieved by modifying the chaotic dynamics of the system. With this improvement, our system's memory capacity and basin volumes are expanded a great deal. Some experimental results in comparison with those of a neural network employing a nonmonotonic output function are also shown. Keywords: chaos, globally coupled map, associative memory, nonmonotonic dynamics, spurious memory 2 1 Introduction...

Article
The main aim of this paper is to develop and implement the concept of incremental learning for fuzzy statistical classifiers. Such a scheme involves continuous modification of training data as learning progresses and is implemented with classifier systems that adapt to incremental information. This paper discusses the implementation of the above approach using a real-time fuzzy classifier system. The recognition performance of this approach on the three spiral benchmark is compared with conventional static training. The paper also discusses the generalisation performance of the system in the context of recognising noisy spiral data. 2 1. Introduction Fuzzy pattern recognition refers to the use of fuzzy mathematical approaches to pattern recognition. The development of fuzzy mathematical approaches for classification purposes is important in several areas including speech analysis, image processing and fuzzy control [21]. This area of research is important to advance the performance o...

Article
Any autonomous system embedded in a dynamic and changing environment must be able to create qualitative knowledge and object structures representing aspects of its environment on the fly from raw or preprocessed sensor data in order to reason qualitatively about the environment. These structures must be managed and made accessible to deliberative and reactive functionalities which are dependent on being situationally aware of the changes in both the robotic agent's embedding and internal environment. DyKnow is a software framework which provides a set of functionalities for contextually accessing, storing, creating and processing such structures. The system is implemented and has been deployed in a deliberative /reactive architecture for an autonomous unmanned aerial vehicle. The architecture itself is distributed and uses real-time CORBA as a communications infrastructure. We describe the system and show how it can be used in execution monitoring and chronicle recognition scenarios for UAV applications.

Article
Part-of-Speech(POS) tagging is a process of assigning a POS to each word in a sentence. Since many words are often ambiguous in their POSs, POS tagging must be able to select the best POS sequence for a given sentence. Recently, probabilistic approaches have shown very promising results to solve such ambiguity problems. Probabilistic approaches, however, usually require lots of training data to get reliable probabilities. To alleviate such restriction, we use fuzzy membership functions instead of probability distributions. Such a POS tagging model is called a fuzzy network POS tagging model. The membership functions are automatically estimated by using probabilities and neural networks with a learning algorithm. Experiments show that the performance of the fuzzy network POS tagging model is much better than that of a hidden Markov model under a limited amount of training data. Keywords : Fuzzy networks, membership function estimation, part-of-speech tagging 1 1 Introductio...

Article
This paper examines the effectiveness of using a quasi-Newton based training of a feedforward neural network for forecasting. We have developed a novel quasi-Newton based training algorithm using a generalized logistic function. We have shown that a well designed feed forward structure can lead to a good forecast without the use of the more complicated feedback /feedforward structure of the recurrent network. keywords: Feed forward neural network, quasi-Newton, forecasting 1 Introduction Many time series have significant chaotic components like short time fluctuations (seasonal variations and cyclical fluctuations), random fluctuations, and long time fluctuations (trend). Stochastic model building and forecasting is one of the techniques for the analysis of discrete time series in the time-domain. Autoregressive Integrated Moving Average (ARIMA) models are examples of these statistical models. These models have largely been linear and as such are not able to capture trends accur...

Article
: A nonlinear servomotor model with friction, saturation, backlash and motor starting voltage is presented in this paper. Fuzzy control and nonlinear PID control are compared using numerous computer simulations. A systematical method for tuning a PD-type fuzzy controller with training data is introduced. 1.0 Introduction Actual servomotors always have nonlinearities that have to be considered in careful controller design. Most important of these are amplifier saturation, load friction, backlash and motor starting voltage. Mathematical analysis that takes all these nonlinearities into account at the same time becomes extremely complex and experimental tuning is still frequently required. 4,6 Complexity is also the main disadvantage of some sophisticated control methods such as adaptive control or friction compensation. 3,5 With fuzzy inference control, however, it is possible to tune an accurate controller without any complicated mathematical analysis, which provides a large number ...

Article
This paper addresses the problem of detecting and identifying persons with a mobile robot, by sensory fusion of thermal and colour vision information. In the proposed system, people are first detected with a thermal camera, using image analysis techniques to segment the persons in the thermal images. This information is then used to segment the corresponding regions of the colour images, using an affine transformation to solve the image correspondence between the two cameras. After segmentation, the region of the image containing a person is further divided into regions corresponding to the person's head, torso and legs. Temperature and colour features are then extracted from each region for input to a pattern recognition system. Three alternative classification methods were investigated in experiments with a moving mobile robot and moving persons in an office environment. The best identification performance was obtained with a dynamic recognition method based on a Bayes classifier, which takes into account evidence accumulated in a sequence of images.

Article
Article
A novel recurrent neural fuzzy network is proposed in this paper. The network model is composed by two structures: a fuzzy system and a neural network. The fuzzy system contains fuzzy neurons modeled with the aid of logic and and or operations processed ...

Article
In this study, we propose fuzzy modeling algorithm to improve Takagi-Sugeno fuzzy model. This algorithm initially finds desirable number of rules at once, in advance, and then identifies the premise and consequent parameters separately by fixing number ...

Article
This Part II of a two-part paper presents the development of a prototype of the INtelligent TRaffic Evaluator for Prompt Incident Diagnosis using a Reality Engine (INTREPID-RE), a system that simulates a traffic accident site using virtual reality. While Part I emphasizes the construction of the user interface control panel and the 3D objects, this paper describes the development of the virtual reality algorithm, environment and models. During the simulation process, novice highway patrol officers wear a head-mounted display and a cyberglove to enter and view an accident scene that they create, and assess the magnitude and severity of the traffic accident. Based on their assessments, patrol officers establish an on-site traffic control strategy and coordinate parking of emergency vehicles that minimizes unnecessary traffic congestion. Officers use INTREPID-RE to secure a safe area for on-site patrol officers, create a channel to permit other drivers to pass the site, and dispatch highway patrol and emergency vehicles to the accident site. The interactive positioning function allows a virtual hand to simulate the positioning of emergency vehicles and clearing of accident vehicles on the road. Six domain experts who evaluated INTREPID-RE rated the system as good and agree that it has the potential to become a promising tool for training novice police officers.

Article
We study n-monotone lower previsions, which constitute a generalisation of n-monotone lower probabilities. We investigate their relation with the concepts of coherence and natural extension in the behavioural theory of imprecise probabilities, and improve ...

Article
In this paper, we develop a relationship between two approaches to combining evidence: the Ordered Abelian Group (OAG) approach and the uninorm approach. We show that while there exist uninorms that are not extended OAG's it turns out that for operations which are continuous (in some reasonable sense), these two approaches coincide.

Article
The paper proposes a fuzzy logic controller for an airplane antilock-braking system (ABS). The system benefits by the knowledge of real vehicle speed, as given by the unbraked front wheel of the landing gear. Thus, the slip ratios of rear wheels can be easily inferred by measuring their angular velocities. By taking into account these slip ratios, resulting from control variables applied in the system, a phenomenological scenario -- a road label inferring diagram -- is conceived to on line decide, via a fuzzy logic reasoning, upon the most suitable new control variable to apply at the current sample step. More precisely, certain threshold values concerns, for each braked wheel, the input variables in the inferring diagram: wheel slip, predicted wheel slip and previous value of control variables. Control variables are thus synthesized in the last component of a standard Mamdani type fuzzy logic control triplet: fuzzifier, rules base and defuzzyfier. A rules base, clustered according to some real road conditions is defined. Supplementary features to improve the braking performance are also described. The simulation results, performed on the mathematical model of a military jet braking, show that proposed ABS algorithm ensures the avoiding of wheel's blockage, even in the worst road conditions.

Article
Fundamental to case-based reasoning is the idea that similar problems have similar solutions. The meaning of the concept of "similarity" can vary in different situations and remains an issue. Since we want to identify and retrieve truly useful or relevant cases for problem solving, the metrics of similarity must be defined suitably to reflect the utility of cases for solving a particular target problem. A framework for utility-oriented similarity modeling is developed in this paper. The main idea is to exploit a case library to obtain adequate samples of utility from pairs of cases. The task of similarity modeling then becomes the customization of the parameters in a similarity metric to minimize the discrepancy between the assessed similarity values and the utility scores desired. A new structure for similarity metrics is introduced which enables the encoding of single feature impacts and more competent approximation of case utility. Preliminary experimental results have shown that the proposed approach can be used for learning with a surprisingly small case base without the risk of over- fitting and that it yields stable system performance with variations in the threshold selected for case retrieval.

Article
In security environments many complicated and interrelated software elements, such as firewalls, network scanners, event distributors and authentication tools, should work cooperatively. The proposed model consists of Multiagent Intrusion Detection System (MIDS) for gathering attack information. It provides a software environment that can afford a generalization/specialization process in order to accomplish attack abstraction. Such a model is designed to detect attacks of several protocols, such as Port Activity, SMTP, HTTP, and FTP. The system changes can be obtained by applying an appropriate security auditing policy. As such MIDS includes four agents; 1) Signature Agent (SA), 2) Network Events Agent (NEA), 3) Vulnerability Scan Agent (VSA) and 4) Intrusion Detection Agent (IDA). These agents are running on each host to be monitored.

Article
Tuning and controlling particle accelerators is time consuming and expensive. Inherently nonlinear, this control problem is one to which conventional methods have not satisfactorily been applied; the result is constant and expensive monitoring by human operators. In recent years, and with isolated successes, advanced information technologies such as expert systems and neural networks have been applied to the individual pieces of this problem. Most advanced information technology attempts are also very special purpose and built in a manner not at all generalizable to other accelerator installations. In this paper, we discuss results of combining various methodologies from the field of artificial intelligence into a control system for accelerator tuning. We consider state space search and adaptive/learning algorithms such as fuzzy logic, rule-based reasoning, neural networks, and genetic algorithms. Finally, we discuss future plans for extending the system to include parallel distributed reasoning, an enhanced object structure, and additional heuristic control methods.

Article
Determining profiles of web portal typical users can be extremely useful, for instance, to personalize the web portal, to provide customized guide and to send tailored advertisements. We present a system to produce a small number of user profiles from the web access log and to associate each user with one of these profiles. The system is based on a version of the Fuzzy C-Means (FCM) algorithm which uses the cosine distance rather than the classical Euclidean distance. After filtering the access log for instance, by removing occasional and undecided users, the FCM algorithm clusters the users into groups characterized by a set of common interests and represented by a prototype, which defines the profile of the group typical member. To attest the validity of these profiles, we extract a set of association rules from the raw access log data by applying the well-known A-priori algorithm and show how the profiles are a concise representation of the association rules. Finally, to test the effectives of the overall fuzzy system, we illustrate how the profiles determined by the FCM algorithm from access log data collected along a period of 30 days allow classifying approximately 93% of the users defined by access log data collected during subsequent 30 days.

Article
This Part II of a two-part paper presents the development of a prototype of the INtelligent TRaffic Evaluator for Prompt Incident Diagnosis using a Reality Engine (INTREPID-RE), a system that simulates a traffic accident site using virtual reality. While Part I emphasizes the construction of the user interface control panel and the 3D objects, this paper describes the development of the virtual reality algorithm, environment and models. During the simulation process, novice highway patrol officers wear a head-mounted display and a cyberglove to enter and view an accident scene that they create, and assess the magnitude and severity of the traffic accident. Based on their assessments, patrol officers establish an on-site traffic control strategy and coordinate parking of emergency vehicles that minimizes unnecessary traffic congestion. Officers use INTREPID-RE to secure a safe area for on-site patrol officers, create a channel to permit other drivers to pass the site, and dispatch highway patrol and emergency vehicles to the accident site. The interactive positioning function allows a virtual hand to simulate the positioning of emergency vehicles and clearing of accident vehicles on the road. Six domain experts who evaluated INTREPID-RE rated the system as good and agree that it has the potential to become a promising tool for training novice police officers.

Article
The ability to determine an accurate global position has many useful commercial and military applications. Because of the L1 GPS receiver's error sources, it is essential to model them. In this paper, a new approach is presented for improving low cost receivers positioning accuracy with Differential GPS (DGPS) corrections real time prediction using pi-sigma, sigma-pi, recurrent, and parallel recurrent neural networks. Methods validity is verified with experimental data from an actual data collection, before and after Selective Availability (SA) error. The result is a highly effective estimation technique for accurate real time positioning; so that prediction RMS errors were less than 0.40 meter after prediction, independent of SA error. The experimental test results with real data emphasize that total performance of RNN is better than PSNN and SPNN considering trade off between accuracy and speed for DGPS corrections prediction. The performance of proposed Parallel Recurrent Neural Network (PRNN) is compared with RNN in DGPS corrections real time prediction. The experimental results demonstrate which the PRNN has great approximation ability and suitability than RNN; so that the PRNN prediction total RMS error respect to the RNN is improved from 2.7348 to 1.7576 meters for 10 seconds ahead prediction and from 4.0397 to 2.5937 meters for 30 second ahead prediction, respectively.

Article
An efficient pattern recognition system based on soft computing concepts has been developed. A new reliable genetic stereo vision algorithm is used in order to estimate depth of objects without using any point-to-point correspondence. Instead, correspondence of the contours as a whole is required. Invariant breakpoints are located on a shape contour using the colinearity principle. Thus, a localized representation of a shape contour including 3-D moments as well as a chain code can be obtained. This representation is invariant to rotation, translation, scale, and starting point. The system is provided with a neural network classifier and a dynamic alignment procedure at its output. Combing the robustness of neural network classifier with the genetic algorithm capability results in a reliable pattern recognition system which can tolerate high degrees of noise and occlusion levels. The performance of the system has been demonstrated using five different types of aircraft and the experimental results are reported.

Article
A conflict profile represents a conflict situation and is understood as a set of opinions of agents or experts, who are asked to solve some common problem. Because of the autonomy of experts or agents the elements of a conflict profile often differ from each other. Thus their reconciling is needed for determining a proper opinion or solution of the problem. This proper opinion or solution is called a consensus. Susceptibility to consensus is a very important feature of conflict situations. One should say that a conflict situation just "incubates" to a consensus if it is possible to determine an agreement which is suitable for all conflict participants. We say that such conflict is susceptible to consensus. In this paper we present two methods for achieving susceptibility for conflict profiles. The first of them relies on modification of a profile by adding o removing elements so that it becomes susceptible to consensus. The second method allows determining a fuzzy weight function for the profile elements to achieve a new consensus susceptible profile. Some algorithms in the second methods are also worked out.

Article
In this paper we model uncertainty using the so-called rough set approach in which upper and lower approximations of a set of objects are based on equivalence classes determined by attribute values. However, due to imprecision in the information, both the attributes and the resulting decisions are modeled as fuzzy sets. Furthermore, the membership of these fuzzy sets is also fuzzy, creating fuzzy sets of type II. From information of this type, we construct inference rules of unequal strength. The strength of any rule is determined by both its degree of truth and its degree of belief, each of which are obtained from the fuzzy memberships.

Article
To design behaviors of a mobile robot for realizing given tasks, a designer has to make a set of rules which generates a proper action from a state of sensors. In general, however, it is difficult for the designer to make the complete set of rules since the number of rules is very large and the proper action for a given state of sensors is not clear. Therefore, the robot must learn and construct the knowledge base of actions by itself. This paper proposes a learning algorithm to construct the knowledge of action in order to achieve tasks that are given to the mobile robot. The action to achieve a task in an environment is generated by a genetic algorithm. It is also shown that repeating the knowledge extraction will make the construction of the Action Knowledge-Base possible, concerning the task in any situation.

Article
This paper reports research results on the designing of an intelligent controller by means of modeling human operator actions based on a Man-Machine Interaction Computer Experiment. Different disturbance signals are applied to the single and the double-integration systems under human operator control to collect data for training the Adaptive Network Based Fuzzy Inference-System as the Neuro-Fuzzy controller. The Neuro-Fuzzy system was able to replace the human operator even in the case of a random disturbance signal applied to the double integrator system.

Top-cited authors
• Foxconn Electronics
• Ningbo University
• Sichuan Normal University
• Abdul Wali Khan University Mardan
• Istanbul Technical University