R.J. Marks II

Baylor University, Waco, TX, United States

Are you R.J. Marks II?

Claim your profile

Publications (73)72.96 Total impact

  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Particle swarm inversion of large neural networks is a computationally intensive process. By the implementing a modified particle swarm optimizer and neural network in reconfigurable hardware, many of the computations can be preformed simultaneously, significantly reducing compilation time compared to a conventional computer.
    Swarm Intelligence Symposium, 2005. SIS 2005. Proceedings 2005 IEEE; 07/2005
  • Source
    W.E. Combs, J.J. Weinschenk, R.J. Marks II
    [Show abstract] [Hide abstract]
    ABSTRACT: Genomic systems design (GSD) is an outgrowth of the union rule configuration (URC), a propositional logic construct that eliminates the combinatorial problem for rule-based systems. Its architecture is scalable, adaptive and fault-tolerant and is well-suited to multi-criteria decision systems and applications that must deal with sparse and missing data. This novel programming paradigm is similar in architecture to a biological process called symbiogenesis. This biological process is said to facilitate the evolution of new species through the inheritance of genomes from organisms that are participating in symbiotic relationships. This similarity, together with the characteristics of the URC, enables genomic systems design to offer a promising alternative methodology for the design of autonomous agents/robots, fault-tolerant and adaptive control systems, cellular automata and bioinformatics.
    Fuzzy Systems, 2004. Proceedings. 2004 IEEE International Conference on; 08/2004
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The pattern recognition approach to transient stability analysis (TSA) has been presented as a promising tool for online application. This paper applies a recently introduced learning-based nonlinear classifier, the support vector machine (SVM), showing its suitability for TSA. It can be seen as a different approach to cope with the problem of high dimensionality. The high dimensionality of power systems has led to the development and implementation of feature selection techniques to make the application feasible in practice. SVMs' theoretical motivation is conceptually explained and they are tested with a 2684-bus Brazilian system. Aspects of model adequacy, training time, classification accuracy, and dimensionality reduction are discussed and compared to stability classifications provided by multilayer perceptrons.
    IEEE Transactions on Power Systems 06/2004; 19(2-19):818 - 825. DOI:10.1109/TPWRS.2004.826018 · 3.53 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: An unsupervised learning system, implemented as an autonomous agent is presented. A simulation of a challenging path planning problem is used to illustrate the agent design and demonstrate its problem solving ability. The agent, dubbed the ORG, employs fuzzy logic and clustering techniques to efficiently represent and retrieve knowledge and uses innovative sensor modeling and attention focus to process a large number of stimuli. Simple initial fuzzy rules (instincts) are used to influence behavior and communicate intent to the agent. Self-reflection is utilized so the agent can learn from its environmental constraints and modify its own state. Speculation is utilized in the simulated environment, to produce new rules and fine-tune performance and internal parameters. The ORG is released in a simulated shallow water environment where its mission is to dynamically and continuously plan a path to effectively cover a specified region in minimal time while simultaneously learning from its environment. Several paths of the agent design are shown, and desirable emergent behavior properties of the agent design are discussed.
    IEEE Transactions on Fuzzy Systems 03/2004; DOI:10.1109/TFUZZ.2003.822683 · 6.31 Impact Factor
  • Source
    M.A. Ei-Sharkawi, R.J. Marks II
    [Show abstract] [Hide abstract]
    ABSTRACT: Error free measurements are an essential requirement for system monitoring, diagnosis, and control. Measurements can be corrupted or interrupted due to sensor failure, broken links, or bad communication. Control, monitoring and diagnostics cannot operate effectively under these conditions. State estimation has been used in the past for sensors restoration. However, it requires accurate, observable and error free detailed system model. These requirements are often unattainable in hardware settings. In this paper, we propose a completely different concept based on auto-encoding and intelligent system search algorithms. The proposed technique is not model based, is hardware realizable, and is rapid enough for fast action of low inertia electromechanical systems. Hardware experimental results show the effectiveness of this technique for on-line control.
    Diagnostics for Electric Machines, Power Electronics and Drives, 2003. SDEMPED 2003. 4th IEEE International Symposium on; 09/2003
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Given a complicated and computationally intensive underwater acoustic model in which some acoustic measurement is a function of sonar system and environmental parameters, it is computationally beneficial to train a neural network to emulate the properties of that model. Given this neural network model, we now have a convenient means of performing geoacoustic inversion without the computational intensity required when attempting to do so with the actual model. This paper proposes an efficient and reliable method of performing the inversion of a neural network underwater acoustic model to obtain parameters pertaining to the characteristics of the ocean floor, using two different modified version of particle swarm optimization (PSO): two-step (gradient approximation) PSO and hierarchical cluster-based PSO.
    Neural Networks, 2003. Proceedings of the International Joint Conference on; 08/2003
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Preprocessing of data to be learned by a neural network is typically done to improve neural network performance. Output processing is especially important since it directly affects the influence of error in the hidden layers on the error of the neural network output. Principal component analysis is a commonly used preprocessing method that can improve the network performance by reducing the output dimensionality and reducing the number of parameters in a neural network model. Transforming the principal components of the outputs with an orthonormal matrix prior to scaling can further improve network performance.
    Neural Networks, 2003. Proceedings of the International Joint Conference on; 08/2003
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Using array historical data, the readings from a sensor array may be shown to contain sufficient redundancy such that the readings from one or more lost sensors may be able to be accurately estimated from those remaining. This interdependency can be established by an neural network encoder. The encoder is also used in the restoration process. In this paper, we give some examples of sensor restoration for vibration sensors on jet engine and computer traffic data.
    Neural Networks, 2003. Proceedings of the International Joint Conference on; 08/2003
  • Source
    J.J. Weinschenk, R.J. Marks II, W.E. Combs
    [Show abstract] [Hide abstract]
    ABSTRACT: We introduce a novel layered fuzzy architecture that avoids rule explosion. Unlike a single layer union rule configuration (URC) fuzzy system, a layered URC fuzzy system can approximate any surface without the need of burdensome "corrective" terms. Further, we show that the URC fuzzy system is a generalized layered perceptron - an insight that allows one to choose interconnection weights in an intuitive manner with very basic problem knowledge. In some cases, training may not be necessary. Further, the fuzzy linguistic meaning of variables is preserved throughout the layers of the system. The universal approximation property of this architecture is discussed and we demonstrate how a layered URC fuzzy system solves a simple regression problem.
    Neural Networks, 2003. Proceedings of the International Joint Conference on; 08/2003
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The neural network autoencoder is a useful tool for the restoration of missing sensors when enough known sensors with some relation to those missing are available. Through the idea of a contraction mapping, this paper provides some insight into the convergence of several iterative methods of sensor restoration using the autoencoder to some unique answer given a specific operating point (i.e., the known sensor values), regardless of how the missing sensor values are initialized.
    Neural Networks, 2003. Proceedings of the International Joint Conference on; 08/2003
  • Source
    J.J. Weinschenk, W.E. Combs, R.J. Marks II
    [Show abstract] [Hide abstract]
    ABSTRACT: We present a novel mapping whereby a classical fuzzy system, based on an intersection rule configuration (IRC), is converted to a union rule configuration (URC) system. Previous work has demonstrated that URC fuzzy systems avoid rule explosion, where a linear increase in the number of antecedents gives rise to an exponential increase in the number of fuzzy rules. However, there has been some doubt as to the validity of URC systems and previous findings. We resolve lingering questions and prove that any arbitrary IRC system can be converted to a URC system with identical performance. Further, we show that URC systems do avoid rule explosion for many problems. Finally, we note that a URC system is a universal approximator.
    Fuzzy Systems, 2003. FUZZ '03. The 12th IEEE International Conference on; 02/2003
  • Source
    M.U. Hazen, W.L.J. Fox, C.J. Eggen, R.J. Marks II
    [Show abstract] [Hide abstract]
    ABSTRACT: A technique is reviewed for training artificial neural networks to emulate the complicated input-output relationships of an acoustic model. This neural network acoustic model emulator is intended for use in a sonar controller, which may require a large number of forward model runs to determine the optimal sonar setting in a given environment. The neural network can supply sonar performance predictions to high enough fidelity for use in a controller, but with a much reduced computational burden compared to the original acoustic model. Among the challenges of developing control guidelines for highly variable littoral areas is the difficulty in understanding the sensitivity of acoustic response to small changes in environmental or sonar control parameters. An effective sensitivity analysis tool would allow users or automatic control algorithms to place a control emphasis on those parameters that have the greatest effect on sonar response. Additionally, an improved understanding of acoustic sensitivity may lead to improvements in model and controller development. In this paper, the neural networks, originally developed for automatic sonar controllers, are used to explore the sensitivity of the system. Given a properly trained neural network, sensitivity measures can be directly calculated. The neural networks can also be used to visualize the effect of changing environmental and control parameters. A variety of ways in which the neural network structures can be used to examine the sensitivity of the sonar system is presented.
    OCEANS '02 MTS/IEEE; 11/2002
  • [Show abstract] [Hide abstract]
    ABSTRACT: The ongoing deregulation of the energy market increases the need to operate modern power systems close to the security border. This requires enhanced methods for the vulnerability border tracking. The high-dimensional nature of power systems' operating space makes this difficult. However, new multiagent search techniques such as particle swarm optimization have shown great promise in handling high-dimensional nonlinear problems. This paper investigates the use of a new variation of particle swarm optimization to identify points on the security border of the power system, thereby identifying a vulnerability margin metric for the operating point.
    IEEE Transactions on Power Systems 09/2002; 17(3-17):723 - 729. DOI:10.1109/TPWRS.2002.800942 · 3.53 Impact Factor
  • Source
    R.J. Marks II, S. Narayanan
    [Show abstract] [Hide abstract]
    ABSTRACT: We consider the problem of signal restoration when P of every N samples in a discrete time system are uniformly decimated. The degraded signal is an aliased form of the original signal. The aliasing can, in certain cases, be unraveled by application of multiplicative discrete time trigonometric polynomials followed by filtering. The filter output is the restored discrete time signal. Conditions required for this restoration are presented. The condition - and thus the noise sensitivity - of the restoration process is also analyzed.
    Circuits and Systems, 2002. ISCAS 2002. IEEE International Symposium on; 02/2002
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: A sensor array can generate interdependent readings among the sensors. If the dependence is sufficiently strong, the readings may contain redundancy to the degree that the readings from one or more lost sensors may be able to be accurately estimated from those remaining. An autoassociative regression machine can learn the data interrelationships through inspection of historical data. Once trained, the autoassociative machine can be used to restore one or more arbitrary lost sensors if the data dependency is sufficiently strong. Recovery techniques include alternating projection onto convex sets (POCS) and iterative search algorithms
    Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on; 02/2002
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: When the situation arises that only "normal" behavior is known about a system, it is desirable to develop a system based solely on that behavior which enables the user to determine when that system behavior falls outside of that range of normality. A new method is proposed for detecting such novel behavior through the use of autoassociative neural network encoders, which can be shown to implicitly learn the nature of the underlying "normal" system behavior
    Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on; 02/2002
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Swarm intelligence forms the core of a new class of algorithms inspired by the social behavior of insects that live in swarms. Its attractive features include adaptation, robustness and a distributed, decentralized nature, rendering swarm-based algorithms well-suited for routing in wireless or satellite networks, where it is difficult it implement centralized network control. We propose one such routing algorithm, dubbed adaptive swarm-based distributed routing (adaptive-SDR), which is scalable, robust and suitable to handle large amounts of network traffic, while minimizing delay and packet loss
    Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on; 02/2002
  • Source
    R.J. Marks II, A.K. Das, M. El-Sharkawi
    [Show abstract] [Hide abstract]
    ABSTRACT: For omnidirectional wireless broadcast, if a node has sufficient power to broadcast to another node, it also has the ability to broadcast to all closer nodes. This is the advantage of wireless. For the broadcast problem, one node (the source) is required to communicate to all other nodes, by a single transmission to the farthest node or using intermediate hop nodes. For a given node constellation, there exist many wireless connection trees to do this. For a known node constellation, the maximum lifetime of a single tree is equal to the minimum battery life of all the nodes. The battery life is determined by the power, if any, expanded by each node. Reaching nodes far remote takes more power. The broadcast lifetime can be significantly increased by using a plurality of trees switching from tree to tree in accordance to a prescribed duty cycle. Using the viability lemma, we propose an evolutionary team optimization of cooperating systems to determine the best team of broadcast trees - along with specified duty cycles - to maximize the lifetime of a given node constellation with specified battery reserves
    Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on; 02/2002
  • C.A. Jensen, M.A. El-Sharkawi, R.J. Marks II
    [Show abstract] [Hide abstract]
    ABSTRACT: One of the most important considerations in applying neural networks to power system security assessment is the proper selection of training features. Modern interconnected power systems often consist of thousands of pieces of equipment each of which may have an effect on the security of the system. Neural networks have shown great promise for their ability to quickly and accurately predict the system security when trained with data collected from a small subset of system variables. This paper investigates the use of Fisher's linear discriminant function, coupled with feature selection techniques as a means for selecting neural network training features for power system security assessment. A case study is performed on the IEEE 50-generator system to illustrate the effectiveness of the proposed techniques
    IEEE Transactions on Power Systems 12/2001; 16(4-16):757 - 763. DOI:10.1109/59.962423 · 3.53 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Considers the problem of neural network supervised learning when the number of output nodes can vary for differing training data. The paper proposes irregular weight updates and learning rate adjustment to compensate for this variation. In order to compensate for possible over training, an a posteriori probability that shows how often the weights associated with each output neuron are updated is obtained from the training data set and is used to evenly distribute the opportunity for weight update to each output neuron. The weight space becomes smoother and the generalization performance is significantly improved
    Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on; 02/2001