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Abstract

An adaptive neural system (ANS) disclosed herein comprises a processor and an adaptor. The processor includes mainly a neural network whose adjustable weights are divided into nonadaptively and adaptively adjustable weights. The nonadaptively adjustable weights are determined through minimizing or reducing a nonadaptive training criterion in an off-line nonadaptive training. Being constructed with a priori training data, the nonadaptive training criterion is a function of the nonadaptively adjustable weights and the diversity variables associated with typical values of the environmental parameter. During an operation of the adaptive neural system, only the adaptively adjustable weights are adjusted on-line to adapt to the unknown environmental parameter. This adaptive training is achieved by minimizing or reducing an adaptive training criterion. The nonadaptive training allows the ANS to make full advantage of a priori information about the ANS's operating environment and helps the ANS focus on learning about and adapting to the unknown environmental parameter during the adaptive training. In many applications, the adaptively adjustable weights can be selected, without adversely affecting the ANS's performance, such that they appear quadratically in the adaptive training criterion. In this case, the adaptive training criterion has no undesirable local minima and the existing fast algorithms for adaptive linear filters are applicable to the adaptive training.

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First Claim