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- W2364753770 abstract "Process neural networks(PNNs) are networks suitable for processing signal input,whose elementary unit is the process neuron,a newly developed neuron model.The process neuron is different from traditional neurons in nature,but there is an inherent relationship between them.The former can be infinitely approached by the latter with arbitrary precision.Two theorems are presented and proved in this paper,giving two models for approaching corresponding process neurons:the time-domain feature expansion model and the orthogonal decomposition feature expansion model.And two corollaries are given based on the second theorem.Finally,some problems with PNNs are discussed and several research topics suggested.The conclusions are significant to theoretical research on process neurons and PNNs." @default.
- W2364753770 created "2016-06-24" @default.
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- W2364753770 date "2007-01-01" @default.
- W2364753770 modified "2023-09-23" @default.
- W2364753770 title "Theoretical discussion on the process neural network theory" @default.
- W2364753770 hasPublicationYear "2007" @default.
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