R.J. Marks II

Baylor University, Waco, TX, United States

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Publications (108)89.84 Total impact

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    ABSTRACT: ev is an evolutionary search algorithm proposed to simulate biological evolution. As such, researchers have claimed that it demonstrates that a blind, unguided search is able to generate new information. However, anal-ysis shows that any non-trivial computer search needs to exploit one or more sources of knowledge to make the search successful. Search algorithms mine active information from these resources, with some search algo-rithms performing better than others. We illustrate these principles in the analysis of ev. The sources of knowl-edge in ev include a Hamming oracle and a perceptron structure that predisposes the search towards its tar-get. The original ev uses these resources in an evolutionary algorithm. Although the evolutionary algorithm finds the target, we demonstrate a simple stochastic hill climbing algorithm uses the resources more efficiently. Copyright: © 2010 Montañez, Ewert, Dembski, and Marks. This open-access article is published under the terms of the Creative Commons Attribution License, which permits free distribution and reuse in derivative works provided the original author(s) and source are credited.
    01/2010;
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    William A. Dembski, Robert J. Marks II
    JACIII. 01/2010; 14:475-486.
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    ABSTRACT: For a general class of dynamical systems (of which the canonical continuous and uniform discrete versions are but special cases), we prove that there is a state feedback gain such that the resulting closed-loop system is uniformly exponentially stable with a prescribed rate. The methods here generalize and extend Gramian-based linear state feedback control to much more general time domains, e.g. nonuniform discrete or a combination of continuous and discrete time. In conclusion, we discuss an experimental implementation of this theory. Comment: 15 pages
    10/2009;
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    ABSTRACT: We revisit the canonical continuous-time and discrete-time matrix algebraic and matrix differential equations that play a central role in Lyapunov based stability arguments. The goal is to generalize and extend these types of equations and subsequent analysis to dynamical systems on domains other than $\R$ or $\Z$, e.g. nonuniform discrete domains or domains consisting of a mixture of discrete and continuous components. We compare and contrast the standard theory with the theory in this general case.
    10/2009;
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    ABSTRACT: We develop a linear systems theory that coincides with the existing theories for continuous and discrete dynamical systems, but that also extends to linear systems defined on nonuniform time scales. The approach here is based on generalized Laplace transform methods (e.g. shifts and convolution) from the recent work [13]. We study controllability in terms of the controllability Gramian and various rank conditions (including Kalman's) in both the time invariant and time varying settings and compare the results. We explore observability in terms of both Gramian and rank conditions and establish related realizability results. We conclude by applying this systems theory to connect exponential and BIBO stability problems in this general setting. Numerous examples are included to show the utility of these results.
    Electronic Journal of Differential Equations 01/2009; · 0.43 Impact Factor
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    Robert J. Marks II, Ian A. Gravagne, John M. Davis
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    ABSTRACT: In this paper, we develop some important Fourier analysis tools in the context of time scales. In particular, we present a generalized Fourier transform in this context as well as a critical inversion result. This leads directly to a convolution for signals on two (possibly distinct) time scales as well as several natural classes of time scales which arise in this setting: dilated, closed under addition, and additively idempotent. We explore the properties of these time scales and demonstrate the utility of these concepts in discrete convolution, Mellin convolution, and transformations of a random variable.
    Journal of Mathematical Analysis and Applications 01/2008; 340(2):901-919. · 1.05 Impact Factor
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    ABSTRACT: We analyze several examples of switched linear circuits and a switched spring–mass system to illustrate the physical manifestations of regressivity and nonregressivity for discrete and continuous time systems as well as hybrid discrete/continuous systems from a time scales perspective. These examples highlight the role that nonregressivity plays in modeling and applications, and they point out a fascinating dichotomy between purely continuous systems and discrete, continuous, or hybrid systems. We conclude with a physically realizable null space criterion for inducing nonregressivity.
    Mathematical and Computer Modelling. 01/2006;
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    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
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    ABSTRACT: We consider the problem of power controlled minimum frame length scheduling for TDMA wireless networks. Given a set of one-hop transmission requests, our objective is to schedule them in a minimum number of time slots, so that each slot schedule is free of self-interferences and meets desired SINR constraints. Additionally, the transmit power vector corresponding to each slot schedule should be minimal. We consider two different versions of the problem, a per-slot version and a per-frame version, and develop mixed integer linear programming models which can be used for solving the problems optimally. In addition, we propose a heuristic algorithm for the per-slot version.
    INFOCOM 2005. 24th Annual Joint Conference of the IEEE Computer and Communications Societies, 13-17 March 2005, Miami, FL, USA; 01/2005
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    W.E. Combs, J.J. Weinschenk, R.J. Marks II
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    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
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    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; · 2.92 Impact Factor
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    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; · 5.48 Impact Factor
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    IEEE T. Fuzzy Systems. 01/2004; 12:107-122.
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    M.A. Ei-Sharkawi, R.J. Marks II
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    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
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    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
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    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
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    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
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    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
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    J.J. Weinschenk, R.J. Marks II, W.E. Combs
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    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
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    J.J. Weinschenk, W.E. Combs, R.J. Marks II
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    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

Publication Stats

2k Citations
89.84 Total Impact Points

Institutions

  • 2005–2010
    • Baylor University
      • • Department of Computer Science
      • • Department of Electrical & Computer Engineering
      • • Department of Engineering
      Waco, TX, United States
  • 1988–2005
    • University of Washington Seattle
      • Department of Electrical Engineering
      Seattle, WA, United States
  • 2004
    • Centro Universitario da Cidade
      Rio de Janeiro, Rio de Janeiro, Brazil
  • 1996
    • University of Alabama in Huntsville
      Huntsville, Alabama, United States
  • 1991
    • University of Nantes
      Naoned, Pays de la Loire, France
    • University of Florida
      • Department of Electrical and Computer Engineering
      Gainesville, FL, United States