A fuzzy neural network based on fuzzy hierarchy error approach

This paper presents a novel fuzzy neural network which consists of an antecedent network and a consequent network. The antecedent network matches the premises of the fuzzy rules and the consequent network implements the consequences of the rules. In the network learning and training phase, a concise and effective algorithm based on the fuzzy hierarchy error approach is proposed to update the parameters of the network. This algorithm is simple to implement and it does not require as many calculations as some other classic neural network learning algorithms. A model reference adaptive control structure incorporating the proposed fuzzy neural network is studied. Simulation results of a cart-pole balancing system demonstrate the effectiveness of the proposed method     

An improved fuzzy neural network based on T–S model

An improved fuzzy neural network based on Takagi–Sugeno (T–S) model is proposed in this paper. According to characteristics of samples spatial distribution the number of linguistic values of every input and the means and deviations of corresponding membership functions are determined. So the reasonable fuzzy space partition is got. Further a subtractive clustering algorithm is used to derive cluster centers from samples. With the parameters of linguistic values the cluster centers are fuzzified to get a more concise rule set with importance for every rule. Thus redundant rules in the fuzzy space are deleted. Then antecedent parts of all rules determine how a fuzzification layer and an inference layer connect. Next, weights of the defuzzification layer are initialized by a least square algorithm. After the network is built, a hybrid method combining a gradient descent algorithm and a least square algorithm is applied to tune the parameters in it. Simultaneous, an adaptive learning rate which is identified from input-state stability theory is adopted to insure stability of the network. The improved T–S fuzzy neural network (ITSFNN) has a compact structure, high training speed, good simulation precision, and generalization ability. To evaluate the performance of the ITSFNN, we experiment with two nonlinear examples. A comparative analysis reveals the proposed T–S fuzzy neural network exhibits a higher accuracy and better generalization ability than ordinary T–S fuzzy neural network. Finally, it is applied to predict markup percent of the construction bidding system and has a better prediction capability in comparison to some previous models.     

基于改进模糊神经网络的PID参数自整定

针对传统PID整定控制效果差且单纯神经网络整定存在参数学习和调整困难等问题,提出了一种基于改进模糊神经网络的PID参数整定方法。在该方法中,PID控制器的控制参数采用基于Mamdani模型的模糊神经网络进行自适应整定,模糊神经网络参数采用混沌遗传算法离线粗调和BP算法在线细调的方式进行学习和调整,仿真结果表明该整定策略动态响应快、误差控制精度高且网络中各节点及参数物理意义明确。最后分别从模糊规则数的变化及适应度函数的选取两方面提出两种优化方案,仿真结果表明增加模糊规则数或采用不同的适应度函数都有利于进一步减小控制误差。     

基于局部线性聚类算法的模糊建模

提出了一种新的基于T-S模糊模型的建模方法，首先通过一种局部线性聚类算法，自适应确定模糊规则数目及初始T-S模型的前提和结论参数，建立相应的一阶T-S模糊神经网络．并用梯度下降和递推最小二乘混合算法训练网络参数，从而提高建模精度．最后，通过两个仿真实例验证了本文方法的有效性．     

Neuro-f uzzy system modeling based on automatic f uzzy clustering

A neuro-fuzzy system model based on automatic fuzzy clustering is proposed. A hybrid model identification algorithm is also developed to decide the model structure and model parameters. The algorithm mainly includes three parts :1) Automatic fuzzy C-means (AFCM) , which is applied to generate fuzzy rules automatically , and then fix on the size of the neuro-fuzzy network , by which the complexity of system design is reducesd greatly at the price of the fitting capability; 2)Recursive least square estimation ( RLSE) . It is used to update the parameters of Takagi-Sugeno model , which is employed to describe the behavior of the system;3) Gradient descent algorithm is also proposed for the fuzzy values according to the back propagation algorithm of neural network. Finally ,modeling the dynamical equation of the two- link manipulator with the proposed approach is illustrated to validate the feasibility of the method.     

Neuro-fuzzy system modeling based on automatic fuzzy clustering

A neuro-fuzzy system model based on automatic fuzzy dustering is proposed. A hybrid model identification algorithm is also developed to decide the model structure and model parameters. The algorithm mainly includes three parts：1） Automatic fuzzy C-means （AFCM）, which is applied to generate fuzzy rttles automatically, and then fix on the size of the neuro-fuzzy network, by which the complexity of system design is reducesd greatly at the price of the fitting capability; 2） R.ecursive least square estimation （RLSE）. It is used to update the parameters of Takagi-Sugeno model, which is employed to describe the behavior of the system;3） Gradient descent algorithm is also proposed for the fuzzy values according to the back propagation algorithm of neural network. Finally,modeling the dynamical equation of the two-link manipulator with the proposed approach is illustrated to validate the feasibility of the method.     

一种混合学习算法的研究

提出一种基于模糊逻辑和神经网络的自学习网络模型。该模型通过特定的学习算法训练样本，能自动生成模糊逻辑规则，调节输入、输出变量的隶属函数。同时，提出了一种结合自组织学习和ＢＰ学习的混合学习算法──ＢＰＳＯＭ，这种算法比通常的ＢＰ机学习算法收敛性好，速度快。     

一种基于模糊加权型推理法的模糊神经网络

本文在Ｍａｍｄａｎｉ模糊推理法的基础上,给出了改进的模糊加权型推理法－广义模糊加权型推理法。     

基于递阶遗传算法模糊加权神经网络的模糊规则自动获取

针对模糊规则的自动获取一直是模糊系统的一个瓶颈问题，提出一种基于递阶结构的混合编码遗传算法与进化规划相结合的模糊加权神经网络学习新算法，利用该算法同时优化模糊加权神经网络的结构和参数，最后说明了从网络中提取模糊规则的方法，从而自动获得最优的模糊规则。分析和实验结果表明，本文方法在规则提取和分类准确性等方面比其他方法更好。     

基于FCM和RVGA的FNN在非线性辨识中的应用

提出一种新的基于模糊C-均值聚类(FCM)和实值遗传算法(RVGA)的模糊神经网络(FNN)。在对模糊规则进行训练之前,利用模糊C-均值聚类从训练数据中提取出典型数据,以删除野值和协调数据内部冲突。然后利用一种新的实值遗传算法对此典型数据进行训练。此遗传算法的交叉和变异运算均直接对实值进行操作,而不是传统的位操作,因此,可以极大地减少训练时间并实现全局寻优。对非线性函数辨识的仿真实验证明了该方法的优越性。     

一种基于遗传算法的模糊神经网络最优控制

通过对控制系统的过程模拟,提出一种模糊神经网络最优控制方案。离线化部分基于遗传算法,分三阶段实现模糊神经网络控制器结构和参数的优化。在线优化部分通过重构模糊神经网络控制器的去模糊化部分,进一步调整控制规则,实现在线去模糊优化。仿真结果表明该方案优于常模糊控制方案和基于专家经验的模糊神经网络控制方案。     

基于UKF的增长型模糊神经网络设计

为了实现模糊神经网络结构和参数的同时调整,提出一种基于无迹卡尔曼滤波(UKF)的增长型模糊神经网络(UKF-GFNN).首先,利用UKF对模糊神经网络的参数进行调整;然后,设计一种基于隐含层神经元输出强度的模糊规则增长机制,实现模糊神经网络的结构增长;最后,将所提出的增长型模糊神经网络应用于非线性系统建模.实验结果显示,基于UKF的增长型模糊神经网络能够实现结构和参数的自校正,并且具有较高的建模精度.     

Developing soft sensors using hybrid soft computing methodology: a neurofuzzy system based on rough set theory and genetic algorithms

This paper presents a hybrid soft computing modeling approach, a neurofuzzy system based on rough set theory and genetic algorithms (GA). To solve the curse of dimensionality problem of neurofuzzy system, rough set is used to obtain the reductive fuzzy rule set. Both the number of condition attributes and rules are reduced. Genetic algorithm is used to obtain the optimal discretization of continuous attributes. The fuzzy system is then represented via an equivalent artificial neural network (ANN). Because the initial parameter of the ANN is reasonable, the convergence of the ANN training is fast. After the rules are reduced, the structure size of the ANN becomes small, and the ANN is not fully weight-connected. The neurofuzzy approach based on RST and GA has been applied to practical application of building a soft sensor model for estimating the freezing point of the light diesel fuel in fluid catalytic cracking unit.     

Self-organizing adaptive fuzzy neural control for the synchronization of uncertain chaotic systems with random-varying parameters

This paper proposes a self-organizing adaptive fuzzy neural control (SAFNC) for the synchronization of uncertain chaotic systems with random-varying parameters. The proposed SAFNC system is composed of a computation controller and a robust controller. The computation controller containing a self-organizing fuzzy neural network (SOFNN) identifier is the principle controller. The SOFNN identifier is used to online estimate the compound uncertainties with the structure and parameter learning phases of fuzzy neural network (FNN), simultaneously. The structure-learning phase consists of the growing of membership functions, the splitting of fuzzy rules and the pruning of fuzzy rules, and thus the SOFNN identifier can avoid the time-consuming trial-and-error tuning procedure for determining the network structure of fuzzy neural network. The robust controller is used to attenuate the effects of the approximation error so that the synchronization of chaotic systems is achieved.All the parameter learning algorithms are derived based on the Lyapunov stability theorem to ensure network convergence as well as stable synchronization performance. To demonstrate the effectiveness of the proposed method, simulation results are illustrated in this paper.     

A maximum spanning tree-based dynamic fuzzy supervised neural network architecture for classification of murder cases

In this paper, a hybrid algorithm based on maximum spanning tree and dynamic fuzzy neural network is proposed for classification of murder cases. The proposed classification model of criminal law is useful for judges, lawyers or other people who want to determine the guilt and deliver judgment in their cases. The model is trained and tested for sufficient number of court decisions. The experimental results show that the proposed maximum spanning tree-based dynamic fuzzy supervised neural network algorithm overcomes the problem of slow convergence and large computation caused by artificial neural network and fuzzy neural network algorithms. Comparative studies were carried out for a number of different networks and configurations and reported. Simulations are presented to illustrate the performance of the proposed algorithm.     

一种混合学习算法的研究与应用

论文提出一种基于模糊逻辑和神经网络的自学习网络模型和一种结合自组织学习和BP学习的BPSOM混合学习算法。该模型通过BPSOM算法训练样本,能自动生成模糊逻辑规则,调节输入、输出变量的隶属函数;而且该算法比通常的BP学习算法收敛性好,速度快。仿真结果表明,利用该学习网络模型构造的同步发电机励磁控制器,能很好地稳定机端电压。     

A neuro-genetic-simulated annealing approach to the inverse kinematics solution of robots: a simulation based study

In this study, a hybrid intelligent solution system including neural networks, genetic algorithms and simulated annealing has been proposed for the inverse kinematics solution of robotic manipulators. The main purpose of the proposed system is to decrease the end effector error of a neural network based inverse kinematics solution. In the designed hybrid intelligent system, simulated annealing algorithm has been used as a genetic operator to decrease the process time of the genetic algorithm to find the optimum solution. Obtained best solution from the neural network has been included in the initial solution of genetic algorithm with randomly produced solutions. The end effector error has been reduced micrometer levels after the implementation of the hybrid intelligent solution system.     

基于EKF的模糊神经网络快速自组织学习算法*

为了快速地构造一个有效的模糊神经网络,提出一种基于扩展卡尔曼滤波(EKF)的模糊神经网络自组织学习算法。在本算法中,按照提出的无须经过修剪过程的生长准则增加规则,加速了网络在线学习过程;使用EKF算法更新网络的自由参数,增强了网络的鲁棒性。仿真结果表明,该算法具有快速的学习速度、良好的逼近精度和泛化能力。     

An adaptive fuzzy neural network for MIMO system modelapproximation in high-dimensional spaces

An adaptive fuzzy system implemented within the framework of neural network is proposed. The integration of the fuzzy system into a neural network enables the new fuzzy system to have learning and adaptive capabilities. The proposed fuzzy neural network can locate its rules and optimize its membership functions by competitive learning, Kalman filter algorithm and extended Kalman filter algorithms. A key feature of the new architecture is that a high dimensional fuzzy system can be implemented with fewer number of rules than the Takagi-Sugeno fuzzy systems. A number of simulations are presented to demonstrate the performance of the proposed system including modeling nonlinear function, operator's control of chemical plant, stock prices and bioreactor (multioutput dynamical system).     

Fuzzy neural networks-based quality prediction system for sinteringprocess

A hybrid fuzzy neural networks and genetic algorithm (GA) system is proposed to solve the difficult and challenging problem of constructing a system model from the given input and output data to predict the quality of chemical components of the finished sintering mineral. A bidirectional fuzzy neural network (BFNN) is proposed to represent the fuzzy model and realize the fuzzy inference. The learning process of BFNN is divided into off-line and online learning. In off-line learning, the GA is used to train the BFNN and construct a system model based on the training data. During online operation, the algorithm inherited from the principle of backpropagation is used to adjust the network parameters and improve the system precision in each sampling period. The process of constructing a system model is introduced in details. The results obtained from the actual prediction demonstrate that the performance and capability of the proposed system are superior