Dynamic bidirectional associative memory using chaotic neurons

A dynamic bidirectional associative memory (DBAM) with chaotic neurons as nodes is proposed. A learning algorithm based on Pontryagin’s minimum principle makes the DBAM equivalent to any other BAM so far reported. The input selection mechanism gives the DBAM the additional ability of multiple memory access, which is based on the dynamics of the chaotic neuron.

[1]  P.K. Simpson,et al.  Higher-ordered and intraconnected bidirectional associative memories , 1990, IEEE Trans. Syst. Man Cybern..

[2]  Stephen Grossberg,et al.  Absolute stability of global pattern formation and parallel memory storage by competitive neural networks , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

[3]  Hoon Kang.  Multilayer associative neural networks (MANN's): storage capacity versus perfect recall , 1994, IEEE Trans. Neural Networks.

[4]  Heekuck Oh,et al.  Adaptation of the relaxation method for learning in bidirectional associative memory , 1994, IEEE Trans. Neural Networks.

[5]  BART KOSKO,et al.  Bidirectional associative memories , 1988, IEEE Trans. Syst. Man Cybern..

[6]  J J Hopfield,et al.  Neurons with graded response have collective computational properties like those of two-state neurons. , 1984, Proceedings of the National Academy of Sciences of the United States of America.

[7]  Jose B. Cruz,et al.  Two coding strategies for bidirectional associative memory , 1990, IEEE Trans. Neural Networks.

[8]  Xinhua Zhuang,et al.  Better learning for bidirectional associative memory , 1993, Neural Networks.

[9]  Jose B. Cruz,et al.  Guaranteed recall of all training pairs for bidirectional associative memory , 1991, IEEE Trans. Neural Networks.

[10]  Zeng-ou Wang.  A Bidirectional Associative Memory Based on Optimal Linear Associative Memory , 1996, IEEE Trans. Computers.

[11]  Stephen Grossberg,et al.  The ART of adaptive pattern recognition by a self-organizing neural network , 1988, Computer.

[13]  Hon-Son Don,et al.  An analysis of high-capacity discrete exponential BAM , 1995, IEEE Trans. Neural Networks.

[14]  J.-J. Lee,et al.  Finding Multiple Local Minima Using Chaotic Jump , 1998, Int. J. Cooperative Inf. Syst..

[15]  Bart Kosko,et al.  Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence , 1991 .

[16]  J J Hopfield,et al.  Rapid local synchronization of action potentials: toward computation with coupled integrate-and-fire neurons. , 1995, Proceedings of the National Academy of Sciences of the United States of America.

[17]  A V Herz,et al.  Neural codes: firing rates and beyond. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[18]  Lipo Wang,et al.  Learning and retrieving spatio-temporal sequences with any static associative neural network , 1998 .

[19]  John J. Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities , 1999 .

[20]  Masafumi Hagiwara.  Multidirectional associative memory , 1990 .

[21]  Xinhua Zhuang,et al.  A general model for bidirectional associative memories , 1998, IEEE Trans. Syst. Man Cybern. Part B.

[22]  Yee Leung,et al.  Asymmetric bidirectional associative memories , 1994 .