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检索条件"机构=Applied Computational Intelligence Laboratory Department of Electrical and Computer Engineering"

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Sparse correlation kernel analysis and evolutionary algorithm-based modeling of the sensory activity within the rat’s barrel cortex 1st

Sparse correlation kernel analysis and evolutionary algorith...

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1st International Workshop on Pattern Recognition with Support Vector Machines, SVM 2002

作者： Milanova, Mariofanna Smolinski, Tomasz G. Boratyn, Grzegorz M. Zurada, Jacek M. Wrobel, Andrzej Department of Computer Science University of Arkansas at Little Rock Little RockAR72204 United States Computational Intelligence Laboratory Department of Electrical and Computer Engineering University of Louisville LouisvilleKY40292 United States Laboratory of Visual System Department of Neurophysiology Nencki Institute of Experimental Biology 3 Pasteur Street Warsaw02-093 Poland

ISBN: (纸本)354044016X

This paper presents a new paradigm for signal decomposition and reconstruction that is based on the selection of a sparse set of basis functions. Based on recently reported results, we note that this framework is equivalent to approximating the signal using Support Vector Machines. Two different algorithms of modeling sensory activity within the barrel cortex of a rat are presented. First, a slightly modified approach to the Independent Component Analysis (ICA) algorithm and its application to the investigation of Evoked Potentials (EP), and second, an Evolutionary Algorithm (EA) for learning an overcomplete basis of the EP components by viewing it as probabilistic model of the observed data. The results of the experiments conducted using these two approaches as well as a discussion concerning a possible utilization of those results are also provided. © Springer-Verlag Berlin Heidelberg 2002.

关键词： Evolutionary algorithms

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Evolutionary algorithms and rough sets-based hybrid approach to classificatory decomposition of cortical evoked potentials 3rd

Evolutionary algorithms and rough sets-based hybrid approach...

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3rd International Conference on Rough Sets and Current Trends in Computing, RSCTC 2002

作者： Smolinski, Tomasz G. Boratyn, Grzegorz M. Milanova, Mariofanna Zurada, Jacek M. Wrobel, Andrzej Computational Intelligence Laboratory Department of Electrical and Computer Engineering University of Louisville LouisvilleKY40292 United States Department of Computer Science University of Arkansas at Little Rock Little RockAR72204 United States Laboratory of Visual System Department of Neurophysiology Nencki Institute of Experimental Biology 3 Pasteur Street Warsaw02-093 Poland

ISBN: (纸本)9783540442745

This paper presents a novel approach to decomposition and classification of rat’s cortical evoked potentials (EPs). The decomposition is based on learning of a sparse set of basis functions using Evolutionary Algorithms (EAs). The basis functions are generated in a potentially overcomplete dictionary of the EP components according to a probabilistic model of the data. Compared to the traditional, statistical signal decomposition techniques, this allows for a number of basis functions greater than the dimensionality of the input signals, which can be of a great advantage. However, there arises an issue of selecting the most significant components from the possibly overcomplete collection. This is especially important in classification problems performed on the decomposed representation of the data, where only those components that provide a substantial discernibility between EPs of different groups are relevant. In this paper, we propose an approach based on the Rough Set theory’s (RS) feature selection mechanisms to deal with this problem. We design an EA and RS-based hybrid system capable of signal decomposition and, based on a reduced component set, signal classification. © Springer-Verlag Berlin Heidelberg 2002.

关键词： Hybrid systems

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Experimental verification of derivatives adaptive critic based neurocontroller performance on single turbogenerators on the electric power grid

Experimental verification of derivatives adaptive critic bas...

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International Joint Conference on Neural Networks (IJCNN)

作者： G.K. Venayagamoorthy R.G. Harley D.C. Wunsch School of Electrical and Electronic Engineering University of Natal Durban South Africa Applied Computational Intelligence Laboratory University of Missouri Rolla MO USA School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

The design and real-time implementation of derivatives adaptive critic based neurocontrollers that replace the conventional automatic voltage regulators (AVRs) and turbine governors are presented in this paper. The feedback variables to the neurocontroller are completely based on local measurements from the turbogenerator. Experimental verification results are presented to show the superior performance of the derivatives adaptive critic based neurocontroller, compared to the conventional AVR and turbine governor controllers equipped with a power system stabilizer.

关键词： Neurocontrollers Turbines Voltage Regulators Neurofeedback Turbogenerators Programmable control Adaptive control Control systems Power system measurements

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Automatic target localization using microelectrode recordings

Automatic target localization using microelectrode recording...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： R.A. Santiago J. McNames H. Falkenberg K. Burchiel Northwest Computational Intelligence Laboratory Portland State University OR USA Biomedical Signal Processing Laboratory Electrical & Computer Engineering Portland State University OR USA Department of Neurological Surgery Oregon Health and Sciences University OR USA

We describe an algorithm that objectively and automatically identifies target regions in the brain for ablation or stimulation during neurosurgery for Parkinson's disease and other movement disorders. The algorithm uses microelectrode recordings to distinguish between the target and adjacent anatomic structures during stereotactic neurosurgery. This algorithm uses a novel method of signal feature extraction that enables standard classification algorithms such as support vector machines to perform well. The algorithm was validated on microelectrode recordings acquired near the globus pallidus internus and labeled by the neurosurgeon.

关键词： Microelectrodes Surgery Signal processing algorithms Neurosurgery Parkinson's disease Magnetic resonance imaging Neurons Laboratories Support vector machines Support vector machine classification

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A parallel computer-Go player, using HDP method

A parallel computer-Go player, using HDP method

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International Joint Conference on Neural Networks (IJCNN)

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

ISBN: (纸本)0780370449

The game of Go has simple rules to learn but requires complex strategies to play well, and, the conventional tree search algorithm for computer games is not suited for Go program. Thus, the game of Go is an ideal problem domain for machine learning algorithms. This paper examines the performance of a 19/spl times/19 computer Go player, using heuristic dynamic programming (HDP) and parallel alpha-beta search. The neural network based Go player learns good Go evaluation functions and wins about 30% of the games in a test series on 19/spl times/19 board.

关键词： Concurrent computing Neural networks Machine learning algorithms Dynamic programming computational intelligence Laboratories Testing Books Law Legal factors

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ART/SOFM: a hybrid approach to the TSP

ART/SOFM: a hybrid approach to the TSP

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International Joint Conference on Neural Networks (IJCNN)

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

ISBN: (纸本)0780370449

We present a new method of solving large scale travelling salesman problem (TSP) instances using a combination of adaptive resonance theory (ART) and self organizing feature maps (SOFM). We divide our algorithm into three phases: phase one uses ART to form clusters of cities; phase two uses a novel modification of the traditional SOFM algorithm to solve a slight variant of the TSP in each cluster of cities; and phase three uses another version of the SOFM to link all the clusters. The experimental results show that our algorithm finds approximate solutions which are about 13% longer than those reported by the chained Lin Kernighan method for problem sizes of 14,000 cities.

关键词： Subspace constraints Neurons Cities and towns Clustering algorithms Neural networks Resonance Hopfield neural networks Unsupervised learning computational intelligence Laboratories

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A statistical solution to a text decoding challenge problem

A statistical solution to a text decoding challenge problem

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International Joint Conference on Neural Networks (IJCNN)

作者： Xindi Cai Rui Xu V.A. Samaranayake D.C. Wunsch Applied Computational Intelligence Laboratory Department of Electrical and Computer Engineering University of Missouri Rolla MO USA Department of Mathematics and Statistics University of Missouri Rolla MO USA

ISBN: (纸本)0780370449

Given an encoded unknown text message in the form of a three dimensional spatial series generated by the use of four smooth nonlinear functions, we use a method based on simple statistical reasoning to pick up samples for rebuilding the four functions. The estimated functions are then used to decode the sequence. The experimental results show that our method gives a nearly perfect decoding, enabling us to submit a 100% accurate solution to the IJCNN challenge problem.

关键词： Decoding Encoding Binary sequences Random variables computational intelligence Laboratories Distributed computing Mathematics Statistical distributions Smoothing methods

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Evolution of analog circuits on a programmable analog multiplexer array

Evolution of analog circuits on a programmable analog multip...

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IEEE Conference on Aerospace

作者： C.C. Santini R. Zebulumi M.A.C. Pacheco M.R. Vellasco M.H. Szwarcman Department of Electrical Engineering TR Laboratories (Telecommunications Research Laboratories) Edmonton AB Canada Department of Electrical Engineering ICA: Applied Computational Intelligence Laboratory RJ Brazil

ISBN: (纸本)0780365992

This paper describes a reconfigurable platform for exploring the intrinsic evolution of analog hardware. The reconfigurable platform consists of integrated circuits whose internal connections can be programmed by the user. Automatic reconfiguration may potentially be driven by Evolutionary Computation techniques such as Genetic Algorithms to synthesize complex circuits. Field Programmable Gate Arrays (FPGAs) and Field Programmable Analog Arrays (FPAAs) constitute the state of the art in the technology of reconfigurable chips, referring to digital and analog devices respectively. These devices will become the building blocks of a forthcoming class of hardware, that will encompass important features of self-adaptation and self-repairing through automatic reconfiguration. These are essential features for systems that need to perform for a long time in harsh environments such as those employed in space exploration missions. In this paper, the reconfigurable platform architectural details, concept and some preliminary tests are presented.

关键词： Analog circuits Multiplexing Field programmable analog arrays Hardware Field programmable gate arrays Evolutionary computation Genetic algorithms Integrated circuit synthesis Integrated circuit technology Space exploration

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Neural network training for varying output node dimension

Neural network training for varying output node dimension

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International Joint Conference on Neural Networks (IJCNN)

作者： Jae-Byung Jung M.A. El-Sharkawi R.J. Marks R. Miyamoto W.L.J. Fox G.M. Anderson C.J. Eggen Computational Intelligence Applications Laboratory Department of Electrical Engineering University of Washington Seattle WA USA Applied Physics Laboratory University of Washington Seattle WA USA

ISBN: (纸本)0780370449

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.

关键词： Neural networks Training data Neurons Laboratories Supervised learning Multilayer perceptrons Network topology computational intelligence Physics Filling

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Dual heuristic programming excitation neurocontrol for generators in a multimachine power system

Dual heuristic programming excitation neurocontrol for gener...

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Conference Record of the IEEE Industry Applications Society Annual Meeting (IAS)

作者： G.K. Venayagamoorthy R.G. Harley D.C. Wunsch Applied Computational Intelligence Laboratory University of Missouri Rolla MO USA School of Electrical and Electronic Engineering University of Natal-Durban Durban South Africa School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

The design of optimal neurocontrollers that replace the conventional automatic voltage regulators for excitation control of turbogenerators in a multimachine power system is presented in this paper. The neurocontroller design is based on dual heuristic programming (DHP), a powerful adaptive critic technique. The feedback variables are completely based on local measurements from the generators. Simulations on a three-machine power system demonstrate that DHP based neurocontrol is much more effective than the conventional PID control for improving dynamic performance and stability of the power grid under small and large disturbances. This paper also shows how to design optimal multiple neurocontrollers for nonlinear systems, such as power systems, without having to continually online train the neural networks, thus avoiding risks of instability.

关键词： Power system simulation Neurocontrollers Power system stability Power system dynamics Power system measurements Power system control Power systems Regulators Optimal control Automatic voltage control

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