基于神经网络的模糊推理-计算机应用技术专业论文.docxVIP

砸二I二论文 砸二I二论文 基于神经网络的模糊推理 vI 624360 摘要 自Zadeh于1973年提出模糊关系合成法(CRI)，人们相继提出多种改进的 方法，把CRI方法加以扩充。本文运用一种新的模糊神经元构建模糊神经网络， 应用于模糊推理，是对CRI法的推广。 文献[17]提出了一种新的基于弱T范数簇的模糊神经元，它能实现多利·连续 逻辑运算。在此神经元基础上，本文构建了三个模糊神经网络，分别实现单重模 糊推理、多维模糊推理和多重模糊推理。其中在多重模糊推理神经网络中，通过 求解模糊关系方程的方法来确定网络的权值。这种模糊推理方法无论对什么样的 蕴涵关系几乎总能满足模糊推理的一致性要求。通过调整权值，该方法还具有灵 活性和适应性。分析它们的性质，发现它们具有连续性和单调性。依此新方法实 现的模糊推理机，其推理效果较好，推理结果鲁棒性低，模糊度相对较小。这一 方法为模糊工业控制系统等提供了一种新的有力的推理工具。 关键词：模糊推理，模糊神经元，神经网络，模糊关系方程，CRI 硕士论文 硕士论文 基于神经网络韵模糊推理 ABSTRACT Since Zadeh introduced the compositional rule of fuzzy inference(CRI)in 1 973， various methods have been proposed to improve it．This paper applies a new fuzzy neuron to constructed fuzzy neural networks，and they are applied to fuzzy inference． This new method is generalized form of CRI． In[17]，a new fuzzy neuron model was presented based on weak T—norm cluster， which can realize many continuous logic operations．Consisting of the new neuron， three fuzzy neural networks are constructed in this paper,they are applied to fuzzy inference，multiple conditional fuzzy inference and multidimensional fuzzy inference respectively．In the neural network corresponding to multidimensional fuzzy inference． the weights arE：adjusted by means of solution to fuzzy relation equation．Based on these neural networks，the new method of fuzzy inference overcomes a few shortcoming of the conventional CRI，and it is much easier to satisfy consistency principle of fuzzy inference than CRI．Analyzed the properties of the new method，we discovered that it is continuous and monotonic．The paper implements fuzzy inference engines with the new method，the reasoning results prove better performance obtained than CRI．The new method proposed here is a powerful inference tool for fuzzy controI systems etc． Keywords：Fuzzy Inference，Fuzzy Neuron，Neural Network，Fuzzy Relation Equation，CRI II 硕L论文 硕L论文 基于神经网络的模糊推理 第一章 绪论 模