Particle Swarm Intelligence to Optimize the Learning of N-tuples

M.A. Hannan Bin Azhar,; F. Deravi,; K.R. Dimond,

doi:10.1515/JISYS.2008.17.S1.169

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Particle Swarm Intelligence to Optimize the Learning of N-tuples

M.A. Hannan Bin Azhar, , F. Deravi, und K.R. Dimond,

Veröffentlicht/Copyright: 1. Dezember 2008

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Aus der Zeitschrift Journal of Intelligent Systems Band 17 Heft Supplement

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Azhar,, M.A. Hannan Bin, Deravi,, F. and Dimond,, K.R.. "Particle Swarm Intelligence to Optimize the Learning of N-tuples" Journal of Intelligent Systems, vol. 17, no. Supplement, 2008, pp. 169-196. https://doi.org/10.1515/JISYS.2008.17.S1.169

Azhar,, M., Deravi,, F. & Dimond,, K. (2008). Particle Swarm Intelligence to Optimize the Learning of N-tuples. Journal of Intelligent Systems, 17(Supplement), 169-196. https://doi.org/10.1515/JISYS.2008.17.S1.169

Azhar,, M., Deravi,, F. and Dimond,, K. (2008) Particle Swarm Intelligence to Optimize the Learning of N-tuples. Journal of Intelligent Systems, Vol. 17 (Issue Supplement), pp. 169-196. https://doi.org/10.1515/JISYS.2008.17.S1.169

Azhar,, M.A. Hannan Bin, Deravi,, F. and Dimond,, K.R.. "Particle Swarm Intelligence to Optimize the Learning of N-tuples" Journal of Intelligent Systems 17, no. Supplement (2008): 169-196. https://doi.org/10.1515/JISYS.2008.17.S1.169

Azhar, M, Deravi, F, Dimond, K. Particle Swarm Intelligence to Optimize the Learning of N-tuples. Journal of Intelligent Systems. 2008;17(Supplement): 169-196. https://doi.org/10.1515/JISYS.2008.17.S1.169

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Published Online: 2008-12

Artikel in diesem Heft

CONTENTS
Editorial: Cybernetic systems: Fuzzy, Neural and Evolutionary Computing Approaches
Nature Inspired Neural Network Ensemble Learning
Cybernetic Approach to Modeling Greenhouse Dynamics
Automatic Regularization of Multilayered-Perceptron Training by Weight Orthogonalization
Genetic Algorithm-based Modeling and Optimization of Control Parameters of an Air Motor
Genetic Programming Control of an Articulated Robotic Manipulator
Genetic Algorithm Optimization and Control System Design of Flexible Structures
Particle Swarm Intelligence to Optimize the Learning of N-tuples
State Estimation Methods for Sound Environment System Based on a Fuzzy Probability Theory

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Artikel in diesem Heft

CONTENTS
Editorial: Cybernetic systems: Fuzzy, Neural and Evolutionary Computing Approaches
Nature Inspired Neural Network Ensemble Learning
Cybernetic Approach to Modeling Greenhouse Dynamics
Automatic Regularization of Multilayered-Perceptron Training by Weight Orthogonalization
Genetic Algorithm-based Modeling and Optimization of Control Parameters of an Air Motor
Genetic Programming Control of an Articulated Robotic Manipulator
Genetic Algorithm Optimization and Control System Design of Flexible Structures
Particle Swarm Intelligence to Optimize the Learning of N-tuples
State Estimation Methods for Sound Environment System Based on a Fuzzy Probability Theory