模糊類神經多項參數建構之預測模式及PSO-RLSE演算法於股市投資策略之研究

Prediction Model of Multiple Fuzzy Neural Parameter on PSO-RLSE Algorithm in Stock Market Investment Strategy

10.6295/TAMJ.202002_20(1).0002

侯夆霖(Fenglin Hou)；李俊賢(Chunshien Li)

類神經網路 ； 模糊類神經模型 ； 粒子群演算法 ； artificial neural networks ； fuzzy-neural system ； particle swarm optimization

台灣管理學刊

20卷1期（2020 / 02 / 01）

23 - 75

繁體中文

大數據時代中影響股市因素眾多使股價預測變得困難。研究目的希望幫助股市投資者採適當策略。通常傳統模型僅用不多的變量預測第二天收盤價，但是模型當用多種變量來改善效能。本研究有五種不同變量用於模型輸入。我們提的模型是一種結合Takagi-Sugeno模糊模型與類神經網絡的模糊神經系統。我們用PSO-RLSE演算法優化所提模型參數，此法結合粒子群演算法與遞歸最小平方演算法。三個實驗使用多種實際數據，實驗結果與文獻比較，結果表明本研究法具有良好的表現。

In the era of big data, various factors affecting the stock market make stock forecasting difficult. The goal of the study hopes to help decision makers take appropriate strategy in stock investments. In most traditional models, one or few variables are usually used to predict the closing stock price of next day. However, a forecasting model should use various variables to improve the forecast. In this study, five different variables are used as inputs to the proposed model. The proposed model is a neuro-fuzzy system (NFS) combining a Takagi-Sugeno fuzzy system with a neural network, whose performance is compared with that of traditional neural network. For parameter learning, a PSO-RLSE composite algorithm, combining the particle swarm optimization (PSO) with the recursive least squares estimator (RLSE), is used to optimizing the parameters of the proposed model. Several real-world data sets have been used to validate the model's efficacy in three experiments. The experimental results have been shown and compared with the previous literature. The results have indicated that the proposed approach has good performancei n time series prediction.

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