| 摘要 |
A robust neural system for robust processing is disclosed for averting unacceptable or disastrous processing performances. This robust neural system either comprises a neural network or comprises a neural network and at least one range transformer. At least one adjustable weight of the robust neural system is a nonlinear weight of the neural work determined in a nonadaptive training of the robust neural system with respect to a nonadaptive risk-sensitive training criterion. If all the adjustable weights of the robust neural system are nonadaptively adjustable, all these weights are held fixed during the robust neural system's operation. If said neural network is recursive, and the nonadaptive training data used to construct said nonadaptive risk-sensitive training criterion contain data for each of a number of typical values of an environmental parameter, the robust neural system is not only robust but also adaptive to the environmental parameter. If the robust neural system comprises both nonadaptively and adaptively adjustable weights, these adaptively adjustable weights are adjusted by an adaptor in the robust neural system during its operation. Such a robust neural system is called a robust adaptive neural system. Two types of adaptor are described.
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