20 August 1992 New on-line adaptive algorithm for nonlinear system identification and control
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Abstract
Neural-based nonlinear system identification and control suffers from the problem of slow convergence, and selection of a suitable architecture for a problem is made through trial and error. There is the need for an algorithm that would provide an efficient solution to these problems. This paper presents one possible solution. Unlike the backpropagation algorithm that trains a fixed structure, in the algorithm presented in this paper, the network is built slowly in a step-by-step fashion. This evolving architecture methodology permits an optimal allocation of hidden nodes that avoids training on outliers and at the same time, provides sufficient complexity for the approximation of a data set. Through simulation examples we show that this algorithm also exhibits faster convergence properties than the usual multi- layered neural network algorithms. Finally, we examine some common ideas between this architecture and fuzzy logic systems.
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Girish Govind, P. Ramamoorthy, "New on-line adaptive algorithm for nonlinear system identification and control", Proc. SPIE 1706, Adaptive and Learning Systems, (20 August 1992); doi: 10.1117/12.139943; https://doi.org/10.1117/12.139943
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