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ABSTRACT: Tooth bleaching is receiving an increasing interest by patients and dentists since it is a relatively non-invasive approach for whitening and lightening teeth. Instrument designed for tooth color measurements and visual assessment with commercial shade guides are nowadays used to evaluate the tooth color. However, the degree of color change after tooth bleaching varied substantially among studies and currently, there are no objective guidelines to predict the effectiveness of a tooth bleaching treatment. Fuzzy logic is a well known paradigm for data modelling; their main advantage is their ability to provide an interpretable set of rules that can be later used by the scientists. However these models have the problem that the global approximation optimization can lead to a deficient rule local modelling. This work proposes a modified fuzzy model that performs a simultaneous global and local modelling. This property is reached thanks to a special partitioning of the input space in the fuzzy system. The proposed approach is used to approximate a set of color measurements taken after a bleaching treatment using the pre-bleaching measurements. The system uses as rule antecedents the colorimetric values of the VITA commercial shade guide. The expected post-bleaching colorimetric values are immediately obtained from the local models (rules) of the system thanks to the proposed modified fuzzy model. Additionally, these post-bleaching CIELAB coordinate values have been associated with VITA shades through the evaluation of their respective membership functions, approximating which VITA shades are expected after the treatment for each pre-bleaching VITA shade.
Intelligent Systems Design and Applications, 2009. ISDA '09. Ninth International Conference on; 01/2010
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ABSTRACT: Radial basis function neural networks (RBFNNs) are well known because, among other applications, they present a good performance when approximating functions although their design still remains as a difficult task. The function approximation problem arises in the construction of a control system to optimize the process of the mineral reduction. In order to regulate the temperature of the ovens and other parameters, a module to predict the final concentration of mineral that will be obtained from the source materials is necessary. In a previous work, this problem was successfully solved by designing an RBFNN using a MultiObjective genetic algorithm (MOGA). However, the more samples are obtained from the system, the more difficult it becomes to design the RBFNN due to the high dimensionality of the problem. Therefore, a new algorithm that addresses the dimensionality reduction has been developed, allowing to obtain more accurate RBFNNs, deciding which input parameters must be considered. Another important element incorporated in the algorithm is the concept of fuzzy dominance, the algorithm, when performing the sorting of the population dividing it in subsets of non-dominated individuals, uses a fuzzy criteria to decide if an individual dominates another. As the experimental results will show, the new version of the algorithm generates RBFNNs with smaller approximation errors and less complexity due to the reduction in the number of input variables and neurons.
Fuzzy Systems Conference, 2007. FUZZ-IEEE 2007. IEEE International; 08/2007
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ABSTRACT: In this paper, the multigrid-based fuzzy system (MGFS) approach is applied for the CATS time series prediction benchmark. The MGFS architecture overcomes the problem inherent to all grid-based fuzzy systems when dealing with high dimensional input data, thus keeping low computational cost and high performance. A greedy algorithm for MGFS structure identification allows to perform the input variable selection for the time series prediction problem, while identifying the pseudo-optimal architecture according to the provided dataset.
Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on; 08/2004
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ABSTRACT: The goal of this paper is to achieve real time control of a monotonic system which, in general, may be non-linear and whose differential equations are unknown. We assume that there is no model of the plant available so there cannot be any off-line pre-training of the main controller parameters. We propose a both adaptive and self-learning algorithm capable of starting from a "void" fuzzy controller and, in real time, optimizing the fuzzy controller's rules (both antecedents and consequents) in order to translate the state of the plant to the desired value in the shortest possible time.
Fuzzy Systems, 2004. Proceedings. 2004 IEEE International Conference on; 08/2004
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ABSTRACT: A novel approach to achieve real-time global learning in fuzzy controllers is proposed. Both the rule consequents and the membership functions defined in the premises of the fuzzy rules are tuned using a one-step algorithm, which is capable of controlling nonlinear plants with no prior offline training. Direct control is achieved by means of two auxiliary systems: The first one is responsible for adapting the consequents of the main controller's rules to minimize the error arising at the plant output, while the second auxiliary system compiles real input-output data obtained from the plant. The system then learns in real time from these data taking into account, not the current state of the plant but rather the global identification performed. Simulation results show that this approach leads to an enhanced control policy thanks to the global learning performed, avoiding overfitting.
IEEE Transactions on Fuzzy Systems 05/2004; · 4.26 Impact Factor
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ABSTRACT: This paper presents a multiobjective evolutionary algorithm to optimize radial basis function neural networks (RBFNNs) in order to approach target functions from a set of input-output pairs. The procedure allows the application of heuristics to improve the solution of the problem at hand by including some new genetic operators in the evolutionary process. These new operators are based on two well-known matrix transformations: singular value decomposition (SVD) and orthogonal least squares (OLS), which have been used to define new mutation operators that produce local or global modifications in the radial basis functions (RBFs) of the networks (the individuals in the population in the evolutionary procedure). After analyzing the efficiency of the different operators, we have shown that the global mutation operators yield an improved procedure to adjust the parameters of the RBFNNs.
IEEE Transactions on Neural Networks 02/2003; 14(6):1478-95. · 2.95 Impact Factor
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ABSTRACT: The identification of a model is one of the key issues in the
field of fuzzy system modeling and function approximation theory. There
are numerous approaches to the issue of parameter optimization within a
fixed fuzzy system structure but no reliable method to obtain the
optimal topology of the fuzzy system from a set of input-output data.
This paper presents a reliable method to obtain the structure of a
complete rule-based fuzzy system for a specific approximation accuracy
of the training data, i.e., it can decide which input variables must be
taken into account in the fuzzy system and how many membership functions
(MFs) are needed in every selected input variable in order to reach the
approximation target with the minimum number of parameters
IEEE Transactions on Fuzzy Systems 07/2002; · 4.26 Impact Factor
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ABSTRACT: Most genetic algorithm (GA) users adjust the main parameters of
the design of a GA (crossover and mutation probability, population size,
number of generations, crossover, mutation, and selection operators)
manually. Nevertheless, when GA applications are being developed it is
very important to know which parameters have the greatest influence on
the behavior and performance of a GA. The purpose of this study was to
analyze the dynamics of GAs when confronted with modifications to the
principal parameters that define them, taking into account the two main
characteristics of GAs; their capacity for exploration and exploitation.
Therefore, the dynamics of GAs have been analyzed from two viewpoints.
The first is to study the best solution found by the system, i.e., to
observe its capacity to obtain a local or global optimum. The second
viewpoint is the diversity within the population of GAs; to examine
this, the average fitness was calculated. The relevancy and relative
importance of the parameters involved in GA design are investigated by
using a powerful statistical tool, the analysis of the variance (ANOVA)
IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) 03/2002; · 2.01 Impact Factor
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ABSTRACT: This paper presents an innovative approach to self-adaptation of
the structure of a fuzzy controller in real time. Without any off-line
pretraining, the algorithm achieves very high control performance
through the iteration of a three-stage algorithm. In the first stage,
coarse tuning of the fuzzy rules (both rule consequents and membership
functions of the premises) is accomplished using the sign of the
dependency of the plant output with respect to the control signal and an
overall analysis of the main operating regions. In stage two, fine
tuning of the fuzzy rules is achieved based on the controller output
error using a gradient-based method. Finally, the third stage is
responsible of modifying the structure of the fuzzy controller,
proposing that input variable which should get a new membership function
in order to improve the control policy in an optimum way
Fuzzy Systems, 2002. FUZZ-IEEE'02. Proceedings of the 2002 IEEE International Conference on; 02/2002
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ABSTRACT: The Web newspaper pagination problem consists of optimizing the layout of a set of articles extracted from several Web newspapers and sending it to the user as the result of a previous query. This layout should be organized in columns, as in real newspapers, and should be adapted to the client Web browser configuration in real time. This paper presents an approach to the problem based on simulated annealing (SA) that solves the problem on-line, adapts itself to the client's computer configuration, and supports articles with different widths.
IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) 02/2002; 32(5):686-91. · 3.08 Impact Factor
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ABSTRACT: An enhanced self-organizing controller (E-SOC) is presented to achieve real time global learning in fuzzy controllers. Direct control is achieved by means of two auxiliary systems: the first one is responsible for adapting the consequents of the main controller's rules to minimize the error arising at the plant output, while the second auxiliary system compiles real input/output data obtained from the plant. The system then learns in real time from these data taking into account, not the current state of the plant but rather the global identification performed. Simulation results show that this approach leads to an enhanced control policy thanks to the global learning performed.
Fuzzy Systems, 2001. The 10th IEEE International Conference on; 01/2002
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ABSTRACT: This paper presents a reliable method to obtain the structure of a
complete rule-based fuzzy system for a specific approximation accuracy
of the training data, i.e. it can decide which input variables must be
taken into account in the fuzzy system and how many membership functions
are needed in every selected input variable in order to reach the
approximation target with the minimum number of parameters
Fuzzy Systems, 2001. The 10th IEEE International Conference on; 02/2001
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ABSTRACT: In this paper we propose a genetic algorithm (GA) that is capable
of simultaneously optimizing the structure of the system and tuning the
parameters that define the fuzzy system. For this purpose, we use the
concept of multiple-deme GAs, in which several populations with
different structures (number of input variables) evolve and compete with
each other. In each of these populations, the element also has different
numbers of membership functions in the input spaces and different
numbers of rules. Instead of the normal coding system used to represent
a fuzzy system, in which all the parameters are represented in vector
form, we performed coding by means of multidimensional matrices, in
which the elements are real-valued numbers, rather than the traditional
binary or gray coding
Fuzzy Systems, 2001. The 10th IEEE International Conference on; 02/2001
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ABSTRACT: This paper proposes a framework for constructing and training a
radial basis function (RBF) neural network, which is an example of fuzzy
system. For this purpose, a sequential learning algorithm is presented
to adapt the structure of the network, in which it is possible to create
a new hidden unit (rule) and also to detect and remove inactive units.
The structure of the gaussian functions (membership functions) is
modified using a pseudo-Gaussian function (PG) in which two sealing
parameters σ are introduced, which eliminates the symmetry
restriction and provides the neurons in the hidden layer with greater
flexibility with respect to function approximation. Other important
characteristics of the proposed neural system is that instead of using a
single parameter for the output weights, these are functions of the
input variables which leads to a significant reduction in the number of
hidden units compared with the classical RBF network Finally, we examine
the result of applying the proposed algorithm to time series prediction
Fuzzy Systems, 2001. The 10th IEEE International Conference on; 02/2001
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ABSTRACT: A new approach is presented to fine tune a fuzzy controller based
on very limited information on the nonlinear plant to be controlled.
Without any off-line pre-training, the algorithm achieves very high
control performance through a two-stage algorithm. In the first stage,
coarse tuning of the fuzzy rules (both rule consequents and membership
functions of the premises) is accomplished in real time using a small
amount of information about the plant and an overall analysis of the
main operating regions. In stage two, fine tuning of the fuzzy rules is
achieved based on the controller output error using a gradient-based
method
Fuzzy Systems, 2001. The 10th IEEE International Conference on; 02/2001
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ABSTRACT: We propose a framework for constructing and training a radial basis function (RBF) neural network. The structure of the gaussian functions is modified using a pseudo-gaussian function (PG) in which two scaling parameters sigma are introduced, which eliminates the symmetry restriction and provides the neurons in the hidden layer with greater flexibility with respect to function approximation. We propose a modified PG-BF (pseudo-gaussian basis function) network in which the regression weights are used to replace the constant weights in the output layer. For this purpose, a sequential learning algorithm is presented to adapt the structure of the network, in which it is possible to create a new hidden unit and also to detect and remove inactive units. A salient feature of the network systems is that the method used for calculating the overall output is the weighted average of the output associated with each receptive field. The superior performance of the proposed PG-BF system over the standard RBF are illustrated using the problem of short-term prediction of chaotic time series.
International Journal of Neural Systems 11/2000; 10(5):353-64. · 4.28 Impact Factor
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ABSTRACT: In this paper, a systematic design is proposed to determine fuzzy
system structure and learning its parameters, from a set of given
training examples. In particular, two fundamental problems concerning
fuzzy system modeling are addressed: 1) fuzzy rule parameter
optimization and 2) the identification of system structure (i.e., the
number of membership functions and fuzzy rules). A four-step approach to
build a fuzzy system automatically is presented: Step 1 directly obtains
the optimum fuzzy rules for a given membership function configuration.
Step 2 optimizes the allocation of the membership functions and the
conclusion of the rules, in order to achieve a better approximation.
Step 3 determines a new and more suitable topology with the information
derived from the approximation error distribution; it decides which
variables should increase the number of membership functions. Finally,
Step 4 determines which structure should be selected to approximate the
function, from the possible configurations provided by the algorithm in
the three previous steps. The results of applying this method to the
problem of function approximation are presented and then compared with
other methodologies proposed in the bibliography
IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) 07/2000; · 3.08 Impact Factor
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ABSTRACT: Describes a structure to create a RBF neural network. This structure has 4 main characteristics. The first one is that the special RBF network architecture uses regression weights to replace the constant weights normally used. These regression weights are assumed to be functions of input variables. The second characteristic is the normalization of the activation of the hidden neurons (weighted average) before aggregating the activations, which, as observed by various authors, produces better results than the classical weighted sum architecture. The third aspect is that a new type of nonlinear function is proposed, the pseudo-gaussian function (PGBF). With this, the neural system gains flexibility, as the neurons possess an activation field that does not necessarily have to be symmetric with respect to the centre or to the location of the neuron in the input space. In addition to this new structure, we propose, as the fourth and final feature, a sequential learning algorithm, which is able to adapt the structure of the network, with this, it is possible to create new hidden units and also to detect and remove inactive units
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on; 02/2000
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ABSTRACT: A general problem in model selection is to obtain the right
parameters that make a model fit observed data. For a multilayer
perceptron (MLP) trained with backpropagation (BP), this means finding
the right hidden layer size, appropriate initial weights and learning
parameters. The paper proposes a method (G-Prop-II) that attempts to
solve that problem by combining a genetic algorithm (GA) and BP to train
MLPs with a single hidden layer. The GA selects the initial weights and
the learning rate of the network, and changes the number of neurons in
the hidden layer through the application of specific genetic operators.
G-Prop-II combines the advantages of the global search performed by the
GA over the MLP parameter space and the local search of the BP
algorithm. The application of the G-Prop-II algorithm to several real
world and benchmark problems shows that MLPs evolved using G-Prop-II are
smaller and achieve a higher level of generalization than other
perceptron training algorithms, such as QuickPropagation or RPROP, and
other evolutive algorithms, such as G-LVQ. It also shows some
improvement over previous versions of the algorithm
Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on; 02/1999
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ABSTRACT: In this paper we design an on-line controller which is able to modify and adapt the rule base of the system with just only qualitative knowledge about the plant to be controlled. Since flying a helicopter is an extremely difficult task, the fuzzy logic controller was necessarily quite complex. In fact, the control tasks were distributed over four individual control units, each of which had its own rules and associated membership functions. Because the fuzzy logic controller was large, and because the rules implemented in the individual control units were not necessarily those a human pilot would use, an efficient technique for writing the rules was required. A genetic algorithm was used to discover rules that provided for effective control of the helicopter. Our study is focused on the module responsible for controlling the helicopter's altitude. For the simulations performed by the adaptive controller, we modify, in a dynamic way, the value of the mass of the helicopter. This would correspond, in real life, to an increase or decrease, for example, in the number of passengers, discharge of water in a fire, etc. On the basis of the nominal value of the helicopter's mass, various simulations are performed to modify the latter parameter within a 15% range. Faced with such a situation, the values of the consequences of the rules responsible for controlling the helicopter's altitude must vary, as otherwise it would not be possible to maintain a zero difference between the desired altitude and that measured by the sensors. Finally, due to the speed requirement, the controller is implemented in FPGA
Advanced Motion Control, 2006. 9th IEEE International Workshop on; 02/2001
