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一种新的模糊神经元网络学习方法
A New Learning Method of Fuzzy Neural Network
【摘要】 提出了一种模糊神经元网络的学习算法即利用多层模糊IF/THEN规则表达专家知识的神经网络学习方法。在以此构造的基于多源信息融合的分类系统中,采用了多层模糊IF/THEN规则进行分类。为了处理模糊语言值,提出了一种能够控制模糊输入矢量的神经网络体系结构,该方法能够对非线性实间隔矢量和模糊矢量进行分类。工程实验表明,此学习算法是切实可行的。
【Abstract】 A learning algorithm of fuzzy neural network has been presented in this paper ,namely, neural network which learns through fuzzy IF/THEN rules. IF/THEN rules and Digital Data are used in the classification system based on the multi-source information fusion. A fuzzy neural architecture is given to process fuzzy input vectors and output fuzzy value. A method that can clarify given non-linear real intervals or fuzzy vectors is given. From our experiments, we can know its feasibility.
【关键词】 信息融合;
非线性模糊分类;
模糊神经元网络;
【Key words】 Information fusion; Non-linear classification; Fuzzy neural network;
【Key words】 Information fusion; Non-linear classification; Fuzzy neural network;
【基金】 国家自然科学基金项目(69873007)
- 【文献出处】 计算机工程 ,Computer Engineering , 编辑部邮箱 ,2002年01期
- 【分类号】TP183
- 【被引频次】2
- 【下载频次】90