Attractor switching by neural control of chaotic neurodynamics
Frank Pasemann and Nico Stollenwerk
Contact the author: Please use for correspondence this email.
Submission date: 12. Feb. 1998
published in: Network, 9 (1998) 4, p. 549- 561
DOI number (of the published article): 10.1088/0954-898X/9/4/009
Download full preprint: PDF (646 kB), PS ziped (233 kB)
Chaotic attractors of discrete-time neural networks include infinitely many unstable periodic orbits, which can be stabilized by small parameter changes in a feedback control. Here we explore the control of unstable periodic orbits in a chaotic neural network with only two neurons. Analytically a local control algorithm is derived on the basis of least squares minimization of the future deviations between actual system states and the desired orbit. This delayed control allows a consistent neural implementation, i.e. the same types of neurons are used for chaotic and controlling modules. The control signal is realized with one layer of neurons, allowing selective switching between different stabilized periodic orbits. For chaotic modules with noise random switching between different periodic orbits is observed.