๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

Automatic learning in chaotic neural networks

โœ Scribed by Masataka Watanabe; Kazuyuki Aihara; Shunsuke Kondo


Publisher
John Wiley and Sons
Year
1996
Tongue
English
Weight
445 KB
Volume
79
Category
Article
ISSN
1042-0967

No coin nor oath required. For personal study only.


๐Ÿ“œ SIMILAR VOLUMES


Controlling chaos in chaotic neural netw
โœ Shin Mizutani; Takuya Sano; Tadasu Uchiyama; Noboru Sonehara ๐Ÿ“‚ Article ๐Ÿ“… 1998 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 277 KB ๐Ÿ‘ 2 views

This work demonstrates the control of chaos in chaotic neural networks. Chaotic neural networks, which were proposed by Aihara and others, consist of chaotic neuron models, and are based on research on the giant axon of squids and study of the Hodgkin-Huxley equation. They show a chaotic response th

Automatic steering of ships using neural
โœ M. A. Unar; D. J. Murray-Smith ๐Ÿ“‚ Article ๐Ÿ“… 1999 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 413 KB ๐Ÿ‘ 2 views

Ship steering control system design presents challenges because the dynamic properties of the vessel itself vary signi"cantly. The use of an arti"cial neural network as a controller which incorporates the properties of a series of conventional controllers designed for di!erent operating conditions c

Experimental study on arranging music by
โœ Tomomasa Nagashima; Jun Kawashima ๐Ÿ“‚ Article ๐Ÿ“… 1997 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 328 KB ๐Ÿ‘ 2 views

Based on the recalling ability on dynamic (chaotic) associative memory of neural networks, we have proposed two methods for making variations of an original melody. By computer simulations, we have shown candidates for the variation of the original melody taken from the first 16 bars of Minuet G maj

DESIGN AND LEARNING WITH CELLULAR NEURAL
โœ NOSSEK, JOSEF A. ๐Ÿ“‚ Article ๐Ÿ“… 1996 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 752 KB

The template coefficients (weights) of a CNN which will give a desired performance, can either be found by design or by learning. 'By design' means that the desired function to be performed can be translated into a set of local dynamic rules, while 'by learning' is based exclusively on pairs of inpu