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International Conference on Instrumentation, Measurement, Circuits and Systems (ICIMCS 2011)

Chen Ming
Chen Ming
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Fuzzy inference systems have long been used for modeling non-linear, uncertain and ambiguous systems in variety of engineering applications. However, these systems suffer from lower accuracy of forecasting due to their intuitive and subjective designs. A modified genetic algorithm is used in this paper to enhance the forecasting accuracy of fuzzy systems. Optimization of system design and automatic generation of fuzzy rule-base have been discussed. A chaotic time series, obtained by solving the Mackey-Glass differential equation, has been used to evaluate performance of the optimized fuzzy system. Simulation results clearly show improvement in the accuracy of fuzzy forecasting.

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