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Optimisation of neural fuzzy control system - having multi-stage learning process in which three level neural network is used together with fuzzy logic rules
Optimisation of neural fuzzy control system - having multi-stage learning process in which three level neural network is used together with fuzzy logic rules
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机译:神经模糊控制系统的优化-具有多级学习过程,其中三级神经网络与模糊逻辑规则一起使用
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摘要
The neural network based fuzzy logic controller has a three-level structure. Located between two of the levels (I,II) are minimal networks for learning a set of three membership functions. The inputs (1,2) in the first level connect with pairs of elements (3,4 and 5,6), and with further elements (7,8,9 and 10,11,12). Between the second and third levels (II,III) are the inference elements of the neuro-fuzzy system. AND functions are provided by nine neurons (13-21). Outputs are generated by elements (22,24) that provide a 'defuzzyfying' action. The process operates to continuously 'learn' until an optimum condition is achieved. USE/ADVANTAGE - Simplifies utilisation of expert system.
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