Training Algorithm of Process Neural Network Based on the Optimal Approximating Piecewise Function
XU Shaohua
LI Yulong
LIU Zhigang
Abstract：A learning algorithm of process neural network based on the optimal approximating piecewise function is pro-posed .the time-varying input signals and connection weights in the network is represented as a fitting form of the piecewise function under a certain accuracy .According to minimum mean square error approach ,learning algorithm of PNN based on function basis expansion is built .It chooses low-order piecewise function as the basis function ,uses its good flexible approxi-mation and smooth nature to rapidly implement implementation adaptive learning of the network undetermined parameters on the function sample .In the network training ,it can reduce redundancy in the model parameters and improve the modeling a-bility of the PNN to the actual problems effectively by just iterative adjusting the connection coefficient of the piecewise func-tion .
Keywords：process neural networktraining algorithmpiecewise functionoptimal approximating
Publication Date：2014-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 919-923 )