深入研究模糊逻辑的必要性与紧迫性

因为"取大取小"不是数学计算,所以基于"取大取小"的模糊逻辑不能为数值转换提供算法支撑,使得模糊理论面临无合适模型可用的被动境地.指出,模糊逻辑是逻辑的一个新的近似推理研究方向,它的量化方法是数值计算;目的是支撑隶属度转换,使得由指标隶属度确定的目标隶属度是"真值"在当前条件下的最优近似.模糊逻辑是在隶属度转换条件下对人类近似推理本领规范的一种方法.而进行规范的依据是区分权滤波的冗余理论,实质性计算是由冗余理论导出的、实现隶属度转换的非线性去冗算法;相应的隶属度转换模型是非线性数学模型.

The Necessity and Urgency of Researching the Fuzzy Logic

The fuzzy logic based on "big or small" can't provide algorithm support for the numerical conversion,for the "big or small" isn't a mathematical calculation.Thus the fuzzy theory is faced with the passive situation tham no suitable model can be used.Fuzzy logic is a new research direction of approximate reasoning.The quantization method is the numerical calculation.The objective is to support the membership conversion,so that the target membership degree determined by the index membership degree is the optimal approximation of the "true value" in the current condition.Therefore,the fuzzy logic is a method to regulate the approximate reasoning ability under the condition of membership conversion.The basis is the redundancy theory of the discrimination power filter.The substantial calculation is the nonlinear redundancy algorithm which derived by the redundancy theory and realized by the membership conversion.The nonlinear redundancy algorithm is the nonlinear mathematical model of the membership conversion.