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This paper proposes time series forecasting using a new feature selection method based on the non-overlap area distribution measurement method and Takagi's and Sugeno's fuzzy model. The non-overlap area distribution measurement method selects the minimum number of 4 input features with the highest performance result from 12 initial input features by removing the worst input features one by one. This...
Particle swarm optimization (PSO) algorithm and fuzzy neural network (FNN) system has been widely used to solve complex decision making problems in practice. However, both of them more or less suffer from the slow convergence,"black-box" and occasionally involve in a local optimal solution. To overcome these drawbacks of PSO and FNN, in this study an improved particle swarm optimization...
In order to realization electronic parts product appearance quality detection control, one kind of processor based on the intelligent knowledge automatic extraction and system intelligence modeling was presented. In the processor, wavelet-fuzzy technique and neural network technique are combined. Uses the fuzzy wavelet extraction image feature, and wavelet function is used as fuzzy membership function...
Fuzzy neural networks have been successfully applied to generate predictive rules for stocks forecasting. This paper presents a methodology for forecasting the daily Korea composite stock price index (KOSPI) based on the neural network with weighted fuzzy membership functions (NEWFM) and time series of KOSPI based on the defuzzyfication of weighted average method (The fuzzy model suggested by Takagi...
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