Abstract
Aiming at the boundedness of existing methods of selecting membership functions, an adaptive Gaussian cloud transform algorithm which is guided by the threshold values of hybridization degree is proposed to construct concept hierarchy from original sample data, and then the number, shape and coverage area of membership functions can be derived from the distribution of Gaussian cloud. To test and verify the effectiveness of membership function that is extracted based on adaptive Gaussian cloud transform algorithm, a six-degree-of freedom model of unmanned aerial vehicles(UAV) is constructed, and a fuzzy controller of pitching angle is established with the platform of Simulink. The simulation results show that the fuzzy controller which includes membership functions derived from the distribution of Gaussian cloud transform can achieve perfect control performance of pitching angle and meanwhile obtain good dynamic response characteristics.
Highlights
an adaptive Gaussian cloud transform algorithm which is guided by the threshold values of hybridization degree is proposed to construct concept hierarchy
shape and coverage area of membership func⁃ tions can be derived from the distribution of Gaussian cloud
verify the effectiveness of membership func⁃ tion that is extracted based on adaptive Gaussian cloud transform algorithm
Summary
概念到定量的数据集合的转换,具体算法如下: Input:数字特征 ( Ex,En,He) ,云滴个数 N; Output:云滴 drop( xi,ui) ,i = 1,2,...,N; Step1 生成以 En 为期望值,He2 为方差的高斯 随机数 En′i = NORM( En,He2) ; Step2 生成以 Ex 为期望值,En′i 2 为方差的高 斯随机数 xi = NORM( Ex,En′i 2) ; Step3 去获取表示定性概念的数字特征,具体算法如下: Input:样本点 xi,i = 1,2,...,N; Output:定性概念的数字特征( E^ x,E^ n,H^ e) ; Step1 计算样本点 xi 的期望值
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