Alexander V.Golovan, Natalia A.Shevtsova, Arkadi Klepatch
A.B.Kogan Research Institute for Neurocybernetics,
Rostov State University, Rostov-on-Don, Russia
Biologically plausible model of the system with an adaptive behavior in a priori uncertain environment and resistant to impairment has been developed. The system consists of input (sensory), learning, and output (motor control) subsystems. The first subsystem classifies input patterns presented as n-dimensional vectors in accordance with some associative rule. The second one being a neural network determines adaptive responses of the system to input patterns. Arranged neural groups coding possible input patterns and appropriate output responses are formed during learning by means of negative reinforcement. Output subsystem maps a neural network activity into the system behavior in the environment.
The system developed has been studied by computer simulation imitating a collision-free motion of a mobile robot. After some learning period the system "moves" along a road without collisions. It is shown that in spite of impairment of some neural network elements the system functions reliably after relearning. Foveal visual preprocessor model developed earlier has been tested to form of a kind of visual input to the system.
Keywords: Neural network, adaptive behavior, foveal visual preprocessor