Evolving fuzzy clusters

An alternate training technique for the fuzzy min-max clustering neural network is introduced. The original fuzzy min-max clustering neural network utilized an algorithm similar to leader clustering and adaptive resonance theory to place hyperboxes in the pattern space. An alternative clustering technique is introduced, which utilizes evolutionary programming and information criteria to produce a set of hyperboxes.<<ETX>>

[1]  J. W. Atmar,et al.  Speculation on the evolution of intelligence and its possible realization in machine form. , 1976 .

[2]  Patrick K. Simpson,et al.  Fuzzy min-max neural networks. I. Classification , 1992, IEEE Trans. Neural Networks.

[3]  Alex Fraser,et al.  Simulation of Genetic Systems by Automatic Digital Computers I. Introduction , 1957 .

[4]  P. K. Simpson,et al.  Fuzzy min-max neural networks , 1991, [Proceedings] 1991 IEEE International Joint Conference on Neural Networks.

[5]  Enrique H. Ruspini,et al.  A New Approach to Clustering , 1969, Inf. Control..

[6]  M. Conrad.  Evolutionary learning circuits. , 1974, Journal of theoretical biology.

[7]  Patrick K. Simpson.  Fuzzy min-max classification with neural networks , 1991, [1991 Proceedings] IEEE Conference on Neural Networks for Ocean Engineering.

[8]  Patrick K. Simpson,et al.  Fuzzy min-max neural networks - Part 2: Clustering , 1993, IEEE Trans. Fuzzy Syst..

[9]  John A. Hartigan,et al.  Clustering Algorithms , 1975 .

[10]  James C. Bezdek,et al.  Some Non-Standard Clustering Algorithms , 1987 .

[11]  Stephen Grossberg,et al.  A massively parallel architecture for a self-organizing neural pattern recognition machine , 1988, Comput. Vis. Graph. Image Process..

[12]  L. Wang,et al.  Fuzzy systems are universal approximators , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.

[13]  W. Vent,et al.  Rechenberg, Ingo, Evolutionsstrategie — Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. 170 S. mit 36 Abb. Frommann‐Holzboog‐Verlag. Stuttgart 1973. Broschiert , 1975 .

[14]  H. Akaike.  A new look at the statistical model identification , 1974 .

[15]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[16]  J. S. F. Barker,et al.  Simulation of Genetic Systems by Automatic Digital Computers , 1958 .

[17]  Thomas Bäck,et al.  A Survey of Evolution Strategies , 1991, ICGA.

[18]  David B. Fogel.  An information criterion for optimal neural network selection , 1991, IEEE Trans. Neural Networks.

[19]  Lawrence J. Fogel,et al.  Artificial Intelligence through Simulated Evolution , 1966 .

[20]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[21]  David B. Fogel,et al.  System Identification Through Simulated Evolution: A Machine Learning Approach to Modeling , 1991 .

[22]  F. Girosi,et al.  Networks for approximation and learning , 1990, Proc. IEEE.

[23]  Jorma Rissanen,et al.  Universal coding, information, prediction, and estimation , 1984, IEEE Trans. Inf. Theory.

[24]  D. B. Fogel,et al.  AN INFORMATION CRITERION FOR OPTIMAL NEURAL NETWORK SELECTION , 1990, 1990 Conference Record Twenty-Fourth Asilomar Conference on Signals, Systems and Computers, 1990..

[25]  J. Reed,et al.  Simulation of biological evolution and machine learning. I. Selection of self-reproducing numeric patterns by data processing machines, effects of hereditary control, mutation type and crossing. , 1967, Journal of theoretical biology.

[26]  David B. Fogel,et al.  Evolving artificial intelligence , 1992 .

[27]  Patrick K. Simpson,et al.  Artificial Neural Systems: Foundations, Paradigms, Applications, and Implementations , 1990 .

[28]  Patrick K. Simpson,et al.  Fuzzy min-max neural networks for function approximation , 1993, IEEE International Conference on Neural Networks.

[29]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .