T-S fuzzy neural network algorithm application in nonlinear control. Sang Y J,et al. 2010ISECS International Colloquium on Computing,Communication,Control,and Management (CCCM) . 2010TS fuzzy neural network algorithm application in nonlinear control. SANG Y,XU C,LIU B,et al. Proceedings of the...
1)T-S fuzzy neural networkT-S模糊神经网络 1.This method first analyzes the photo data,and uses T-S fuzzy neural network to process them,then uses the D-S evidence theory to make decision analysis,ultimately,obtains effective result with highly precise recognition.该方法先对输入图像进行数据分析...
This article integrated rule expression capacity of fuzzy logic inference with self-learning ability of the neural network, proposed to build Takagi-Sugeno fuzzy neural network's quantitative identification of mixed gas by combining T-S fuzzy neural network with neural network. The results indicated ...
目前,确定权重的方法很多,其中T-S模糊神经网络(Takagi-Sugeno Fuzzy Neural Network Model)作为一种常用的多指标决策评估工具已经成功应用于许多数据分析领域。但是,这种方法却很少用于软件可信度评估中。因此,本文基于模糊理论特别是T-S模糊神经网络提出了软件过程可信性评估模型。 1软件项目的可信度概述 1.1软件项目的...
whichovercame local minimum of traditionalgradient descent method.Keywords plant diseases; image segmentation; fuzzy neuralnetwork; quantum genetic algorithm; decision system 模糊系统的 T S 模型[ 1]是模糊规则后件为输入语言变量的函数, 该模型能够有效的发挥模糊系统和神经网络各自的优势, 高效地解决模糊系统...
网络和T-S模糊模型,本文采用T-S动态递归型模 糊神经网络(dynamiCT—Srecurrentfuzzyneural networks,DTRFNN 唱】。利用递归网络实现模糊推 理,能很好地反映动态映射关系,并具定性知识表 达能力,使网络的内部知识具有明确的物理意义, 且可较易地确定网络的结构和神经元的参数。
an improved T–Sfuzzy neural network(TSFNN) is introduced to predict BOD values by the soft computing method. In this improved TSFNN, a K-means clustering is used to initialize the structure of TSFNN, including the number offuzzy rulesand parameters of membership function. For training TSFNN...
Based on the idea of the knowledge reduction of the rough sets (RS) theory and the nonlinearity mapping of Takagi-Sugeno fuzzy neural network (FNN), a kind of RS-FNN intelligent control method is presented and applied in the rotary kiln sintering process due to its nonlinearities in the dyna...
Based on the glowworm swarm optimization (GSO) and T-S fuzzy neural network (TSFNN), this paper proposes a prediction algorithm for traffic flow of T-S fuzzy neural network optimized glowworm swarm optimization (GSOTSFNN). The proposed algorithm uses GSO to get the optimal parameter configuratio...
Keywords: finite-time boundedness; discrete-time; T-S fuzzy model; Lyapunov-Krasovskii functional; stochastic jumping neural networks 1 Introduction Over the past decades, an enormous number of works have been significant as regards various neural networks because of wide applications, such as signal ...