Serve system intelligent control based on a novel associative memory system

This paper firstly proposes a novel associative memory system based on discrete Taylor series-DTS-AMS, which is capable of implementing error-free approximations to multi-variable polynomial functions of arbitrary order. The advantages it offers over conventional CMAC neural network are: high-precision of learning, much smaller memory requirement without the data-collision problem, much less computational effort for training and faster convergence rates than that attainable with multi-layer BP neural networks. Secondly, a serve system intelligent control scheme based on DTS-AMS is designed, where DTS-AMS is employed to learn the inverse dynamic model of the serve system. A set of numerical simulations have been conducted, and simulation results have shown that the novel neural network based control strategy is feasible and efficient. The novel neural network has great potential in the application areas of realtime intelligent control for complex system.