Synthesis of nonlinear control surfaces by a layered associative search network.
An approach to solving nonlinear control problems is illustrated by means of a layered associative network composed of adaptive elements capable of reinforcement learning. The first layer adaptively develops a representation in terms of which the second layer can solve the problem linearly. The adaptive elements comprising the network employ a novel type of learning rule whose properties, we argue, are essential to the adaptive behavior of the layered network. The behavior of the network is illustrated by means of a spatial learning problem that requires the formation of nonlinear associations. We argue that this approach to nonlinearity can be extended to a large class of nonlinear control problems.
D000818:Animals; D001245:Association Learning; D006801:Humans; D007858:Learning; D008433:Mathematics; D008960:Models, Psychological; D012054:Reinforcement, Psychology