[1]HE Xin-gui,XU Shao-hua.Process neural networks and its applications in time-varying information processing[J].CAAI Transactions on Intelligent Systems,2006,1(1):1-8.
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Process neural networks and its applications in time-varying information processing

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 1 Number of periods: 2006 1 Page number: 1-8 Column: 综述 Public date: 2006-03-25
Title:
Process neural networks and its applications in time-varying information processing
Author(s):
HE Xin-gui; XU Shao-hua
National Laboratory on Machine Perception,Peking University, Beijing 100871，China
Keywords:
process neural networks;  time-varying system;  information processing;  learning algorithm;  simulation experiment
CLC:
TP18
DOI:
-
Abstract:
Aimed at the problems of the timevarying information processing and the dynamic system modeling, two kinds of process neural network models, including the rational formula process neural networks and the process neural ne tworks with time-varying inputs and outputs function,were built in this paper. In the process neural networks with timevarying inputs and outputs function, the time accumulation operator of process neuron was adopted as the integral to time or other algebra operations, its spacetime aggregation mechanism and incitation could synchronously reflect the space aggregation and stage time accumulation effect of exterior timevarying input signals to the output results, so as to com plete the complex mapping relationship between the inputs and outputs of non-linear system. In the rational formula process neural networks, its basic information processing unit was made up of two process neurons which appear dually,and logi cally divided into numerator and denominator, then output after rational formula combining, it can effectively advance the flexile approximation of process neural networks to the process functions which have singular values and the facility of reaction nearby the singular value point. The characteristics of these two kinds of process neural networks models were analyzed in this paper, the concrete learning algorithms were given, the effectiveness of these two kinds of network modes in time-varying information processing was proved by the cases of the process simulation and fault diagnosis of rotating machinery in the oil filed exploitation.
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