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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Nian Zhang D.C. Wunsch Applied Computational Intelligence Laboratory Department of Electrical and Computer Engineering University of Missouri Rolla MO USA

Fuzzy attributed graph (FAG) is not only widely used in the fields of image understanding and pattern recognition, but is useful to fuzzy graph matching problem. One of the applications of fuzzy graph matching is the subcircuit extraction problem. Subcircuit extraction problem is very important for VLSI testing, layout versus schematic (LVS) check, and circuit partition, etc. In this paper, fuzzy attributed graph (FAG) is first effectively applied to the subgraph isomorphism problem. And then we provide an efficient fuzzy attributed graph algorithm based on the solution to subgraph isomorphism for the subcircuit extraction problem. Similarity measurement makes a significant contribution to both the subgraph isomorphism problem and the subcircuit extraction problem.

关键词： Circuit testing Partitioning algorithms Fuzzy sets computational intelligence Application software Pattern recognition Pattern matching Very large scale integration Electronics industry Semiconductor device manufacture

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Fuzzy logic in collective robotic search

Fuzzy logic in collective robotic search

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： N. Zhang D.C. Wunsch Applied Computational Intelligence Laboratory Department of Electrical and Computer Engineering University of Missouri Rolla MO USA

One important application of mobile robots is searching a geographical region to locate the origin of a specific sensible phenomenon. We first propose a fuzzy logic approach using a decision table. A novel fuzzy rule based was designed. And then a fuzzy search strategy is adopted by utilizing the three tier centers of mass coordination. Experimental results show that fuzzy logic algorithm is an efficient approach for the collective robots to locate the target source. In addition, noise and the position of the target affect the searching result.

关键词： Fuzzy logic Robot kinematics Robot sensing systems Orbital robotics Search problems Costs Mobile robots Humans Chemical hazards Control systems

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An extended Kalman filter (EKF) approach on fuzzy system optimization problem

An extended Kalman filter (EKF) approach on fuzzy system opt...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Nian Zhang D.C. Wunsch Applied Computational Intelligence Laboratory Department of Electrical and Computer Engineering University of Missouri Rolla MO USA

Optimizing the membership functions of a fuzzy system can be viewed as a system identification problem for a nonlinear dynamic system. Basically, we can view the optimization of fuzzy membership functions as a weighted least-squares minimization problem, where the error vector is the difference between the fuzzy system outputs and the target values for those outputs. The extended Kalman filter algorithm is a good choice to solve this system identification problem, not only because it is a derivative-based algorithm that is suitable to solve the weighted least-squares minimization problem, but also because of its appealing predictor-corrector feature for nonlinear system model. In this paper, we present an extended Kalman filter approach to optimize the membership functions of the inputs and outputs of the fuzzy controller. The effect of the measurement noise covariance R on the convergence of the fuzzy controller is also investigated. Experimental results show that the optimized fuzzy controller achieves significant improvement on performance. In addition, the smaller the measurement noise covariance R is, the faster the optimized fuzzy controller would converge.

关键词： Fuzzy systems Fuzzy control computational intelligence Noise measurement State estimation Nonlinear dynamical systems Minimization methods Predictive models Kalman filters Performance evaluation

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Intrusion detection using radial basis function network on sequences of system calls

Intrusion detection using radial basis function network on s...

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International Joint Conference on Neural Networks (IJCNN)

作者： A. Rapaka A. Novokhodko D. Wunsch Applied Computational Intelligence Laboratory Department of Electrical and Computer Engineering University of Missouri Rolla Rolla MO USA

Over the past few years, security has been an increasing concern, with the growth of network and technological development. An intrusion detection system is a critical component for secure information management. Unfortunately, present IDS's falls short of providing protection required for growing concern. Creation of an IDS to detect anomaly intrusions, in a timely and accurate manner, has been an elusive goal for researchers. This paper describes a host-based IDS model, utilizing a Radial Basis Function neural network. It functions as a combined anomaly/misuse detector that helps to overcome most of the limitations in existing models. Rather than creating user profiles or behavioral characteristics, we trained our network using session data in the identification and tested experimentally on different attack/normal sessions. These results suggest that training the IDS on session data is not only effective in detecting intrusions, but also accurate and timely.

关键词： Intrusion detection Radial basis function networks Pattern matching computational intelligence Laboratories computer security Information security Information management Protection Detectors

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Probabilistic neural networks for multi-class tissue discrimination with gene expression data

Probabilistic neural networks for multi-class tissue discrim...

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International Joint Conference on Neural Networks (IJCNN)

作者： Rui Xu D.C. Wunsch Applied Computational Intelligence Laboratory Department of Electrical and Computer Engineering University of Missouri Rolla Rolla MO USA

With the emergence and rapid advancement of DNA microarray technologies, construction of gene expression profiles for different cancer types has already become a promising means for cancer diagnosis and treatment. Most previous research has focused on binary classification. Here, we use a probabilistic neural network (PNN) for multi-classification of cancer data. The experimental results demonstrate the effectiveness of the PNN in addressing gene expression data.

关键词： Neural networks Gene expression Cancer DNA Neoplasms Fluorescence Medical treatment computational intelligence Laboratories Uncertainty

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The subcircuit extraction problem

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IEEE Potentials 2003年第3期22卷 22-25页

作者： Zhang, Nian Wunsch, II, Donald C. Harary, Frank IEEE United States University of Missouri-Rolla Rolla MO United States Department of Electrical and Computer Engineering University of Missouri Rolla United States Applied Computational Intelligence Laboratory IEEE Neural Networks Society Technical Committee International Neural Networks Society Board of Governors United Kingdom Indian National Academy of Sciences India

No abstract available

关键词： Very large scale integration Circuit testing Microelectronics Area measurement Semiconductor device measurement Size measurement Integrated circuit measurements Investments Automatic testing Design automation

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A comparison of dual heuristic programming (DHP) and neural network based stochastic optimization approach on collective robotic search problem

A comparison of dual heuristic programming (DHP) and neural ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Nian Zhang D.C. Wunsch Applied Computational Intelligence Lab Department of Electrical and Computer Engineering University of Missouri Rolla Rolla MO USA

An important application of mobile robots is searching a region to locate the origin of a specific phenomenon. A variety of optimization algorithms can be employed to locate the target source, which has the maximum intensity of the distribution of some detected function. We propose two neural network algorithms: stochastic optimization algorithm and dual heuristic programming (DHP) to solve the collective robotic search problem. Experiments were carried out to investigate the effect of noise and the number of robots on the task performance, as well as the expenses. The experimental results showed that the performance of the dual heuristic programming (DHP) is better than the stochastic optimization method.

关键词： Robot programming Neural networks Stochastic processes Search problems Robot kinematics Mobile robots Robot sensing systems Heuristic algorithms Orbital robotics computational intelligence

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Neural networks applied to electromagnetic compatibility (EMC) simulations

Neural networks applied to electromagnetic compatibility (EM...

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Joint International Conferences on Artificial Neural Networks and Neural Information Processing, ICANN/ICONIP 2003

作者： Göksu, Hüseyin Wunsch, Donald C. Applied Computational Intelligence Lab Department of Electrical and Computer Engineering University of Missouri-Rolla 1870 Miner Circle G11 Emerson Electric Co. Hall RollaMO65409 United States

ISBN: (纸本)3540404082

Data extrapolation in FDTD simulations using feedforward multilayer Perceptron (MLP) showed promising results in a previous study. This work studies two different aspects of the problem: First is the learning aspect, including the effect of prior training with the same class of random signals, which is an attempt to find a general solution to the weight initialization problem in adaptive systems. The second aspect covers the steps to make the extrapolator fully adaptive, through optimization of the time step sensitivity and the input layer width of a sliding window extrapolator. Average mutual information is used as a performance measure in most of the work. © Springer-Verlag Berlin Heidelberg 2003.

关键词： Electromagnetic compatibility

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Linear least-squares based methods for neural networks learning

Linear least-squares based methods for neural networks learn...

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Joint International Conferences on Artificial Neural Networks and Neural Information Processing, ICANN/ICONIP 2003

作者： Fontenla-Romero, Oscar Erdogmus, Deniz Principe, J.C. Alonso-Betanzos, Amparo Castillo, Enrique Laboratory for Research and Development in Artificial Intelligence Department of Computer Science University of A Coruña Campus de Elviña s/n Coruña15071 A Spain Computational NeuroEngineering Laboratory Electrical and Computer Engineering Department University of Florida GainesvilleFL32611 United States Department of Applied Mathematics and Computational Sciences University of Cantabria University of Castilla-La Mancha Avda de Los Castros s/n Santander39005 Spain

ISBN: (纸本)3540404082

This paper presents two algorithms to aid the supervised learning of feedforward neural networks. Specifically, an initialization and a learning algorithm are presented. The proposed methods are based on the independent optimization of a subnetwork using linear least squares. An advantage of these methods is that the dimensionality of the effective search space for the non-linear algorithm is reduced, and therefore it decreases the number of training epochs which are required to find a good solution. The performance of the proposed methods is illustrated by simulated examples. © Springer-Verlag Berlin Heidelberg 2003.

关键词： Feedforward neural networks

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Intelligent strain sensing on a smart composite wing using extrinsic Fabry-Perot interferometric sensors and neural networks

Intelligent strain sensing on a smart composite wing using e...

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International Joint Conference on Neural Networks (IJCNN)

作者： R. Dua V. Eller K.M. Isaac S.E. Watkins D.C. Wunsch Applied Computational lntelligence Laboratory University of Missouri Rolla Rolla MO USA Applied Optics Laboratory Department of Electrical and Computer Engineering University of Missouri Rolla Rolla MO USA Department of Mechanical and Aerospace Engineering University of Missouri Rolla Rolla MO USA

Strain prediction at various locations on a smart composite wing can provide useful information on its aerodynamic condition. The smart wing consisted of a glass/epoxy composite beam with three extrinsic Fabry-Perot interferometric (EFPI) sensors mounted at three different locations near the wing root. Strain acting on the three sensors at different air speeds and angles-of-attack were experimentally obtained in a closed circuit wind tunnel under normal conditions of operation. A function mapping the angle of attack and air speed to the strains on the three sensors was simulated using feedforward neural networks trained using a backpropagation training algorithm. This mapping provides a method to predict the stall condition by comparing the strain available in real time and the predicted strain by the trained neural network.

关键词： Intelligent sensors Intelligent networks Capacitive sensors Fabry-Perot Neural networks Aerodynamics Glass Circuit simulation Feedforward neural networks Backpropagation

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