Design and Construction of Process Neural Networks

Abstract

As a kind of functional approximator, process pattern associative memory machine and time-varying signal classifier, process neural networks have broad applications in modeling and solving various practical problems related to the time process or multivariate process. For example, Ding and Zhong used a wavelet process neural network to solve time series prediction problems[1],[2], and used a parallel process neural network to solve the problem of aircraft engine health condition monitoring[3]. Zhong et al. used a continuous wavelet process neural network to solve the problem of monitoring of an aero-engine lubricating oil system[4]. Xu et al. used a process neural network and a quantum genetic algorithm to solve oil recovery ratios[5]; Song et al. used a mixed process neural network to predict the churn in mobile communications[6]. In order to solve practical application problems, we must design and construct corresponding process neural networks in terms of concrete problems, including the choice of the network model, the determination of the number of hidden layers and hidden nodes, the selection or the design of the neuron type in each node layer (including the choice of activation function, etc.), and the design of corresponding learning algorithms and parameters, etc.