Research Of ENN Model And Stock Index Futures Analysis Based On It

In recent years, ENN develops rapidly in the field of AI, and there are a large amount of researches. Although starting rather late in the field of stock index futures in China, investment related to stock index futures is very active all the time, and various methods for its prediction outpour. Undoubtedly, these researches provide certain reference value for investors. But for the prediction and analysis for stock index futures are very practical, each prediction method has its emphasis and scope of applications. As a result, ENN models are improved, and they are applied to the prediction and analysis of stock index futures. This not only provides a new method and thought for the stock index futures forecasting analysis field, but also enriches ENN in practical applications.This paper describes the structure and algorithms of two kinds of improved ENN-type 2 in detail, and then the models’feasibility and effectiveness in prediction and analysis of stock index futures are verified through experiments. In this paper, firstly, two kinds of ENN-type 2 models are improved. Secondly, the original data sequence is preprocessed to become the orderly data sequence by using extension transformation of the extension theory. Thirdly, the orderly data is applied to the improved ENN-type 2 models to do prediction and analysis, and the feasibility of the algorithms is proved by experiments. Finally, compared with the experiment results of ENN-type 2 and BP neural network, the improved ENN-type 2 models are more feasible and effective in prediction and analysis of stock index futures.The improved ENN-type 2 algorithms provide another novel method for technical personnel in the futures investment company. Moreover, the ENN-type 2 algorithms can be practically applied in the stock index futures decision analysis system, which can guide customers to make better decisions in the futures investment.