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.

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