在学习过程中通过同时调整小波基函数的平移因子和隶属度函数的形状，使得模糊小波网络的精度和泛化能力大大提高。
By adjusting the translation parameters of the wavelets and the shape of membership functions, the accuracy and generalization capability of FWN can be remarkably improved.
为解决此问题，提出一种基于捕食-被捕食的粒子群优化模糊聚类算法且聚类中心采用密度函数初始化。
To solve the problem, a fuzzy clustering based on predator prey PSO algorithm is presented, which is using density function to initialize cluster centre.
算法重点考虑到成像过程中必然引入的各种噪声，用高斯分布函数模糊化直线参数，使提取具有良好的稳健性。
The new method puts emphases on dealing with all kinds of noise from the imaging process, and USES Gaussian distribution to blur parameters of straight lines in order to ensure extraction robustness.
选取的隶属函数使神经网络权值有一定的知识表示意义，并通过模糊化层将输入特征量转化为模糊量。
The selected membership function made neural network weight values have definite knowledge meaning, and the input characteristic variables were translated into fuzzy variables by fuzzy layer.
为使模糊屈服面明朗化，文中引入了与模糊集截集相应的模糊屈服函数族。并巧妙地将隶属函数和屈服函数联系起来。
Fuzzy yield functions corresponding to he cut-set of fuzzy sets are introduced for the obviousness of fuzzy yield surface, and the membership function is ingeniously related with yield function.
在使用函数模板时，如果你定义了多个重载的特例化函数，可能导致模糊不清的调用，所以这时编译器会从中选择最特例的那个函数定义来调用。
A function template specialization might be ambiguous because template argument deduction might associate the specialization with more than one of the overloaded definitions .
本文提出了一种基于模式类特征空间统计分布的模糊隶属度函数模型，可有效地反映模式在特征空间中的真实分布，用于模式分类器输入特征的模糊化可获取更好的识别性能。
In this paper a model of discrete fuzzy membership function based on statistical distribution of features of pattern is presented. It is used for the fuzziness of input features of classifier.
本文提出了一种基于模式类特征空间统计分布的模糊隶属度函数模型，可有效地反映模式在特征空间中的真实分布，用于模式分类器输入特征的模糊化可获取更好的识别性能。
In this paper a model of discrete fuzzy membership function based on statistical distribution of features of pattern is presented. It is used for the fuzziness of input features of classifier.
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