A New Approach to Artificial Immune Systems and its Application in Constructing On-line Learning Neuro-Fuzzy Systems

In this paper, we present an on-line learning neuro-fuzzy system which was inspired by parts of the mecha- nisms in immune systems. It illustrates how an on-line learning neuro-fuzzy system can capture the basic elements of the immune system and exhibit some of its appealing properties. During the learning procedure, a neuro-fuzzy system can be incrementally constructed. We illustrate the potential of the on-line learning neuro-fuzzy system on several benchmark classification problems and function approximation problems.

[1]  John E. Hunt,et al.  Learning using an artificial immune system , 1996 .

[2]  P. K. Simpson.  Fuzzy Min-Max Neural Networks-Part 1 : Classification , 1992 .

[3]  V. Devarajan,et al.  Artificial immune systems and aerial image segmentation , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[4]  Leandro Nunes de Castro,et al.  ARTIFICIAL IMMUNE SYSTEMS: PART II - A SURVEY OF APPLICATIONS , 2000 .

[5]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[6]  Jerome H. Carter.  Research Paper: The Immune System as a Model for Pattern Recognition and Classification , 2000, J. Am. Medical Informatics Assoc..

[7]  Hossam Meshref,et al.  Artificial immune systems: application to autonomous agents , 2000, Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. 'cybernetics evolving to systems, humans, organizations, and their complex interactions' (cat. no.0.

[8]  Jerry M. Mendel,et al.  Fuzzy basis functions: comparisons with other basis functions , 1995, IEEE Trans. Fuzzy Syst..

[9]  Stephanie Forrest,et al.  Architecture for an Artificial Immune System , 2000, Evolutionary Computation.

[10]  Dipankar Dasgupta,et al.  Artificial neural networks and artificial immune systems: similarities and differences , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[11]  Chien-Hsing Chou,et al.  An on-line learning neuro-fuzzy system based on artificial immune systems , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[12]  Yinghua Lin,et al.  A new approach to fuzzy-neural system modeling , 1995, IEEE Trans. Fuzzy Syst..

[13]  Marco Russo,et al.  Genetic fuzzy learning , 2000, IEEE Trans. Evol. Comput..

[14]  L. Segel,et al.  Design Principles for the Immune System and Other Distributed Autonomous Systems , 2001 .

[15]  J Timmis,et al.  An artificial immune system for data analysis. , 2000, Bio Systems.

[16]  Anca L. Ralescu,et al.  An improved synthesis method for multilayered neural networks using qualitative knowledge , 1993, IEEE Trans. Fuzzy Syst..

[17]  H.,et al.  The Immune System as a Model for Pattern Recognition and Classification , 1999 .

[18]  Shigeo Abe,et al.  A method for fuzzy rules extraction directly from numerical data and its application to pattern classification , 1995, IEEE Trans. Fuzzy Syst..

[19]  Leandro Nunes de Castro,et al.  Artificial Immune Systems: Part I-Basic Theory and Applications , 1999 .

[20]  Cheol W. Lee,et al.  Construction of fuzzy basis function networks using adaptive least squares method , 2001, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).

[21]  Dipankar Dasgupta,et al.  Novelty detection in time series data using ideas from immunology , 1996 .

[22]  C. Chou,et al.  A Self-Generating Neuro-Fuzzy System Through Reinforcements , 2002 .

[23]  D. Dasgupta.  Artificial Immune Systems and Their Applications , 1998, Springer Berlin Heidelberg.

[24]  Wei-Yin Loh,et al.  A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-Three Old and New Classification Algorithms , 2000, Machine Learning.

[25]  A. B. Watkins,et al.  A new classifier based on resource limited artificial immune systems , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[26]  L X Wang,et al.  Fuzzy basis functions, universal approximation, and orthogonal least-squares learning , 1992, IEEE Trans. Neural Networks.

[27]  N. Kasabov,et al.  Rule insertion and rule extraction from evolving fuzzy neural networks: algorithms and applications for building adaptive, intelligent expert systems , 1999, FUZZ-IEEE'99. 1999 IEEE International Fuzzy Systems. Conference Proceedings (Cat. No.99CH36315).

[28]  Stephen Grossberg,et al.  Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps , 1992, IEEE Trans. Neural Networks.

[29]  Stephanie Forrest,et al.  Immunity by design: an artificial immune system , 1999 .

[30]  James C. Neidhoefer,et al.  Immunized adaptive critics , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).

[31]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[32]  Jonathan Lee,et al.  A Neuro-Fuzzy Approach for Compensating Color Backlight Images , 2006, Neural Processing Letters.

[33]  Dipankar Dasgupta,et al.  Immunity-Based Intrusion Detection System: A General Framework , 1999 .

[34]  Saman K. Halgamuge,et al.  Neural networks in designing fuzzy systems for real world applications , 1994 .

[35]  Rudolf Kruse,et al.  A neuro-fuzzy method to learn fuzzy classification rules from data , 1997, Fuzzy Sets Syst..

[36]  Satish Kumar,et al.  Subsethood-product fuzzy neural inference system (SuPFuNIS) , 2002, IEEE Trans. Neural Networks.

[37]  Marco Russo,et al.  FuGeNeSys-a fuzzy genetic neural system for fuzzy modeling , 1998, IEEE Trans. Fuzzy Syst..