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• Main Authors: Chi-Feng Lin, 林奇鋒
• Other Authors: 李俊賢
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/xknr5u

碩士 === 國立中央大學 === 資訊管理學系 === 106 === Stock fluctuations are time series data. The prediction of time series is an important research topic. Artificial intelligence models are currently being widely used in this topic, such as neuro-fuzzy systems. This paper proposes a complex neuro-fuzzy system and applies it to multi-target time series prediction. This model has multiple complex-valued outputs, every output can have real and imaginary parts for two different real-valued targets, respectively. With regard to feature selection, this study uses multi-target feature selection to filter out features that are beneficial to all targets and use this as the model inputs to reduce the overall computational burden and improve data utilization efficiency. In terms of model, multi-layer neural network is constructed from input layer, Complex fuzzy set layer (CFS layer), Premise neural layer, Takagi-Sugeno neural layer (T-S neural layer), and output layer. For parameter learning, we use the divide-and-conquer principle when training the model. The parameters of the complex fuzzy set neural layer are optimized using different algorithm, such as particle swarm optimization (PSO), artificial bee colony optimization (ABCO); the parameters of the T-S neural layer are optimized using recursive least-squares estimation (RLSE), other neural layers have no parameters to optimize. In terms of experiments, we use three experiments to test the performance of the model. We combine investment strategy with PSO-RLSE and ABCO-RLSE experimental results, respectively, and calculate model profit to compare with each other and the different literature methods. This study proposed a new investment strategy. Through the results of the performance comparison and the profits comparison, this paper presents a multi-target prediction method showing excellent performance and investment effect.