By Topic

Chaotic dynamics in quasi-layered recurrent neural network model and application to complex control via simple rule

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Yongtao Li ; Sch. of Natural Sci. & Technol., Okayama Univ., Okayama, Japan ; Kurata, S. ; Yoshinaka, R. ; Nara, Shigetoshi

In this paper, chaotic dynamics in quasi-layered recurrent neural network model (QLRNNM), consisting of sensory neurons and motor neurons, is applied to solving ill-posed problems. We would like to emphasize two typical properties of chaos utilized in QLRNNM. One is sensitive response to external signals. The other is complex dynamics of many but finite degree of freedom in high dimensional state space, which can be utilized to generate low dimensional complex motions by a simple coding. Moreover, presynaptic inhibition is introduced to produce adaptive behavior. Using these properties, as an example, a simple control algorithm is proposed to solve two-dimensional maze, which is set as an ill-posed problem. Computer experiments and actual hardware implementation into a roving robot are shown.

Published in:

Neural Networks, 2009. IJCNN 2009. International Joint Conference on

Date of Conference:

14-19 June 2009