Process Neural Networks Theory and Applications proposes theconcept and model of a process neural work for the first time,showing how it expands the mapping relationship between the inputand output of traditional neural works and enhances theexpression capability for practical problems, with broadapplicability to solving problems relating to processes inpractice. Some theoretical problems such as continuity, functionalapproximation capability, and puting capability, are closelyexamined. The application methods, work construction principles,and optimization algorithms of process neural works in practicalfields, such as nonlinear time-varying system modeling, processsignal pattern recognition, dynamic system identification, andprocess forecast, are discussed in detail. The informationprocessing flow and the mapping relationship between inputs andoutputs of process neural works are richly illustrated.
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