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The stock market, which has been investigated by various researchers, is a rather complicated environment. Most research only concerned the technical indexes (quantitative factors), instead of qualitative factors, e.g., political effect. However, the latter plays a critical role in the stock market environment. Thus, this study develops a genetic algorithm based fuzzy neural network (GFNN) to formulate...
In spite of great importance of fuzzy feed-forward and recurrent neural networks (FNN) for solving wide range of real-world problems, today there is no effective learning algorithm for FNN. In this paper we propose an effective genetic-based learning mechanism for FNN with fuzzy inputs, fuzzy weights expressed as LR-fuzzy numbers, and fuzzy outputs. The effectiveness of the proposed method is illustrated...
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