Optimized Polynomial Neural Network Classifier Designed with the Aid of Space Search Simultaneous Tuning Strategy and Data Preprocessing Techniques

  • Huang, Wei (School of Computer and Communication Engineering, Tianjin University of Technology, State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology) ;
  • Oh, Sung-Kwun (Dep. of Electrical Engineering, The University of Suwon)
  • Received : 2016.07.11
  • Accepted : 2016.10.31
  • Published : 2017.03.01


There are generally three folds when developing neural network classifiers. They are as follows: 1) discriminant function; 2) lots of parameters in the design of classifier; and 3) high dimensional training data. Along with this viewpoint, we propose space search optimized polynomial neural network classifier (PNNC) with the aid of data preprocessing technique and simultaneous tuning strategy, which is a balance optimization strategy used in the design of PNNC when running space search optimization. Unlike the conventional probabilistic neural network classifier, the proposed neural network classifier adopts two type of polynomials for developing discriminant functions. The overall optimization of PNNC is realized with the aid of so-called structure optimization and parameter optimization with the use of simultaneous tuning strategy. Space search optimization algorithm is considered as a optimize vehicle to help the implement both structure and parameter optimization in the construction of PNNC. Furthermore, principal component analysis and linear discriminate analysis are selected as the data preprocessing techniques for PNNC. Experimental results show that the proposed neural network classifier obtains better performance in comparison with some other well-known classifiers in terms of accuracy classification rate.


Polynomial Neural Network Classifier (PNNC);Principal component analysis;Linear discriminate analysis;Space search optimization;Simultaneous tuning strategy;Data preprocessing technique


Supported by : National Natural Science Foundation of China, National Research Foundation of Korea (NRF)


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