Adaptive system identification by nonadaptively trained neural networks

This paper proposes a novel adaptive neural system (ANS), which minimizes computation, focuses on learning about and adapting to the unknown environmental parameter, and eliminates (or reduces) poor local minima of the performance surface during the operation of the ANS. The idea is illustrated by its application to adaptive system identification. The adjustable weights of the ANS are divided into nonadaptively and adaptively adjustable weights. The former are determined by a nonadaptive training, using a priori information. Only the latter are adapted in operation. If they consist of linear weights of the ANS, the fast algorithms for adaptive linear filters are applicable for adaptation.