Research on Technological Innovation Talents of the Six Provinces of Mid-China based on BP Artificial Neural Networks of the Golden Section Theory

Abstract

Technological innovation talent is the core element of economic and social development. It is a symbol of a country's soft power reflected from the perspective of knowledge. Therefore it is theoretically valuable and practically significant for a country to evaluate the comprehensive level technological innovation. Based on the construction of evaluation index system of the technological innovation talents’ competitiveness, we will use BP neural network to evaluate in this paper. We determine the actual situation of the evaluation system according to the input layer, hidden layer and output layer of the model as well as the number of neurons in each layer. The number of neurons in the input layer is the number of indices of technological innovation talents evaluation. The output layer represents the comprehensive ability level and has only one neuron. However, it is difficult to figure out the number of hidden layers of BP neural networks. We use the golden section method to accurately select their layers. This can effectively avoid the problem of excessive training error due to the random selection of hidden layer. Then we illustrate the accuracy and usefulness of this algorithm with the case of the technological innovation talents of the six central provinces. Tests show that the comprehensive ability of technological innovation talents of Hubei is the most powerful in the six central provinces and is followed by Anhui. The remaining four provinces have similar levels. The actual development of technological innovation talents agree well with the experimental results.