Polynomial Neural Network Classifiers Based on Data Preprocessing and Space Search Optimization

In this paper, we propose a novel architecture of polynomial neural network classifier (PNNC) with the aid of data preprocessing technique and space search optimization, which adopts accelerated convergence mechanism instead of purely random search. Two type of polynomials are adopted for constructing discriminate functions in the PNNC to alleviate the limitation of relatively simple geometry using linear discriminate function in the conventional neural network classifiers. Space search optimization is exploited here to realize structure optimizes and parameter optimize in the design of PNNC. Moreover, data preprocessing techniques are used to reduce the dimension of training data. The proposed PNNC is compared with some well-known classifiers based on several benchmark data sets. Experimental results illustrate the effectiveness of PNNCs.