| Using BP Neural Network to Determine the Membership Function of the Input of Fuzzy Control System |
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| 作者姓名： | ZHANG Xin-yan |
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| 摘 要： | While design the fuzzy controller, it is very important to determine the membership function of fuzzy variables.The data can be broadly classified as fuzzy sets by using the classification property of the BP neural network. The author selects a BP neural network with one hide layer and uses S function to the input and hide layer, and linear function to the output layer.Advanced BP algorithm isused to train the BP neural network in the environment of MATLAB . The nearer to the target values is the better the last output is.With the trained BP network , the membership values of the inputs can be got ten. This method has high rate and low error.
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| 关 键 词： | BP neural network fuzzy controller membership function fuzzify |
| 修稿时间： | 2004/1/18 0:00:00 |
Using BP Neural Network to Determine the Membership Function of the Input of Fuzzy Control System |
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| ZHANG Xin-yan.Using BP Neural Network to Determine the Membership Function of the Input of Fuzzy Control System[J].Storage & Process,2004(5):54-56. |
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| Authors: | ZHANG Xin-yan |
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| Abstract: | While design the fuzzy controller, it is very important to determine the membership function of fuzzy variables.The data can be broadly classified as fuzzy sets by using the classification property of the BP neural network. The author selects a BP neural network with one hide layer and uses S function to the input and hide layer, and linear function to the output layer.Advanced BP algorithm isused to train the BP neural network in the environment of MATLAB . The nearer to the target values is the better the last output is.With the trained BP network , the membership values of the inputs can be got ten. This method has high rate and low error. |
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| Keywords: | BP neural network fuzzy controller membership function fuzzify |
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