Polynomial Neural Network with Direct Solutions and Its Interpretation of Inputs

Abstract

Abstract: The generalized multivariate polynomial neural network and single-hidden-layer generalized multivariate polynomial neural network were designed and systematically studied, and the existence of the optimal weight vector was proved which could make the network the best approximation polynomial for an unknown function; The concepts of the natural upper and lower nodes in the hidden layer were created, and an indicator “value of importance” was creatively designed and the partial derivative analysis was introduced to solve the problem that the neural network was not able to interpret the relationship between variables. The weight vector directly was solved proving it optimal. In addition, Matlab-based graphical user interface was designed. Through this program, users could update stock data via the stock software, and predicted the stock index on specified date via different models.

Key words: generalized multivariate polynomial neural network, weights-direct-determination, importance value, stock index forecasting

CLC Number:

TP391.9

Shen Wei, Li Qiushi, Song Yukun. Polynomial Neural Network with Direct Solutions and Its Interpretation of Inputs[J]. Journal of System Simulation, 2015, 27(3): 559-570.

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