基于模糊超球的模糊神经网络初始参数设定

在人们常用到的模糊神经学习算法中很少讨论如何设置并调整初始的权值参数,这样就会影响模糊神经学习算法的准确度,使得目标函数值很大。虽然在改进的模糊神经学习算法中使用模糊C均值聚类方法来确定模糊规则的初始参数,但是这种方法必须已知模式集的数目,这就限制了模糊神经网络的应用范围。因此,将模糊超球神经网络的思想与模糊神经网络有机的结合起来,通过不断调整超球的中心和半径以及超球的数目,优化模式集的边界,来确定权值参数,利用这种方法确定初始参数可以减小误差,提高算法的准确度,使目标函数值减小。     

模糊神经网络在数据采掘中的应用研究

提出了一种基于模糊神经网络的数据采掘新方法。该方法首先基于Rough sets思想获取初始规则和训练集，基于采掘属性的数目和分类目标确定网络结构，通过遗传(GA)算法对网络进行优化，通过BP算法实现网络权值的在线调整，最后对所生成的规则进行简化，提取模糊规则。仿真实例结果表明，该方法是行之有效的。     

模糊超球神经网络在模式聚类中的应用

提出和实现了用于模式聚类的无监督模糊超球神经网络.模式集是一个具有超球核的用隶属函数表示的模糊集,模式集又可以合并成模式类.模糊超球神经网络学习算法能在几次循环学习中形成模式集,无需对已知模式集重新训练就可融合新样例和精炼已存在的模式集.模式聚类的数值仿真解释了模糊超球聚类神经网络的优越性能.     

一种基于动态聚类的模糊分类规则的生成方法

介绍了一种基于动态聚类的模糊分类规则的生成方法，这种方法能决定规则数目，隶属函数的位置及形状．首先，介绍了基于超圆雏体隶属函数的模糊分类规则的基本形式；然后，介绍动态聚类算法，该算法能将每一类训练模式动态的分为成簇，对于每簇，则建立一个模糊规则；通过调整隶属函数的斜度，来提高对训练模式分类识别率，达到对模糊分类规则进行优化调整的目的；用两个典型的数据集评测了这篇文章研究的方法，这种方法构成的分类系统在识别率与多层神经网络分类器相当，但训练时间远少于多层神经网络分类器的训练时间．     

基于蚁群聚类的自适应神经网络算法

针对模糊神经网络结构辨识问题,提出了一种基于蚁群聚类的自适应神经网络算法。利用蚁群聚类算法确定模糊神经网络的初始结构,在网络的学习过程中,采用误差反向传播学习算法对网络的参数和结构进行优化,达到自适应调整权值参数和结构的目的。最后,针对非线性函数遥近问题进行了验证。通过仿真试验证明该方法取得了很好的结果,系统的逼近精度明显提高,而且网络的自适应能力强,并可以将其有效地用于模糊建模和控制问题的求解。     

基于模糊神经网络的空中目标威胁估计

将模糊神经网络用于空中目标威胁估计，阐述了网络结构的确定和算法实现过程。由于模糊神经网络不需要对象的准确模型。它以分布的方式存储信息，利用网络的拓扑结构和权值分布实现非线性映射，在神经网络框架下引入模糊规则，使网络中的权值有明显的意义，且保留了神经网络的学习机制，对权值的学习采用改进BP算法。仿真结果表明了该方法的有效性，为进行有效的目标威胁评估提供了一条新的思路。     

基于概率超球集神经网络的发动机故障诊断

提出了一种用于发动机故障检测与诊断的概率超球集神经网络．神经网络用概率集表示发动机故障模式,概率集是由超球聚集形成的集合体,超球是由球心和半径确定．概率超球集神经网络能在两次循环中完成学习过程,并能不断融合新样本信息和精炼已存在的故障模式．YF-20发动机故障检测与诊断的仿真研究验证了概率超球集神经网络分类器的优越性能．     

基于模糊神经网络的飞机某系统故障诊断研究

提出一种基于模糊神经网络的飞机某系统故障诊断方法。利用改进的模糊C均-值聚类算法进行结构辨识,从而自动获得模糊规则库,并得到模糊模型的初始参数;然后生成与之相匹配的初始模糊神经网络,并通过学习算法训练网络来进行参数辨识,得到一个精确的模糊模型。将该系统地面实测数据作为样本数据,建立起了基于模糊神经网络的飞机某系统故障诊断模型。最后对该模型进行测试与分析,结果表明该方法具有抗噪、抗敏感、诊断准确度高等优点。     

CMAC神经网络模糊控制器设计

详细介绍了CMAC神经网络结构、中间层作用函数地址的计算方法、输出层权值的学习算法,并利用CMAC神经网络对水下机器人深度模糊控制器进行了学习。仿真结果表明,训练得到的CMAC神经控制器控制效果良好,中间层作用函数地址的计算方法正确。     

一种新的模糊规则提取方法

提出了一种新的模糊规则提取方法,该方法先采用基于山峰函数的减法聚类法自适应地确定初始的聚类中心,然后由此构造动态自组织神经网络进行学习,在学习的过程中可根据情况适当地合并或分裂神经元,并重构神经网络继续学习,最后按聚类中心确定模糊子集数目和隶属函数并形成模糊规则集.实验结果表明,通过网络结构和神经元的动态自适应变化能够获取样本集中的模糊信息,形成直观的模糊规则.     

Designing neurofuzzy system based on ICART algorithm and its application for modeling jet fuel endpoint of hydrocracking process

In this paper, a neuro-fuzzy system based on improved CART algorithm (ICART) is presented, in which the ICART algorithm is used to design neuro-fuzzy system. It is worth noting that ICART algorithm partitions the input space into tree structure adaptively, which avoids the curse of dimensionality (number of rules goes up exponentially with number of input variables). Moreover, it adopts density function to construct the local model for every node in order to overcome the discontinuous boundaries existed in CART algorithm. In addition, a supervised scheme is used to adjust parameters to minimize the network output error and construct more accurate fuzzy model on the basis of the ICART algorithm. Finally, to illustrate the validity of the proposed method, a simulation research and a practical application are done. The results show that the proposed method can provide optimal model structure and parameters for fuzzy modeling, possesses high learning efficiency, and is smoother than CART algorithm. It can be successfully applied to modeling jet fuel endpoint of hydrocracking processing.     

Symbiotic organisms search algorithm for optimal evolutionary controller tuning of fractional fuzzy controllers

With the development of technology and the practical needs of complex engineering applications, fuzzy controllers have been widely applied. In contrast to a traditional integer-order fuzzy controller, a fractional fuzzy controller can extend the integral and differential order of a fuzzy controller to any real number, which describes the controlled object more accurately and enhances its control performance. However, a fractional fuzzy controller has a larger number of control parameters, which makes it difficult to calibrate. Because the parameter controller tuning values of the fuzzy controller clearly influence its control performance, this paper proposes to optimize the parameter controller tuning process using the symbiotic organisms search algorithm. A large number of simulation tests were carried out to compare the symbiotic organisms search-based parameter controller tuning method with parameter controller tuning based on five other representative swarm intelligence algorithms. The experimental results show that the symbiotic organisms search algorithm better optimizes the parameters of the fractional fuzzy controller.     

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.     

An efficient quantum neuro-fuzzy classifier based on fuzzy entropy and compensatory operation

In this paper, a quantum neuro-fuzzy classifier (QNFC) for classification applications is proposed. The proposed QNFC model is a five-layer structure, which combines the compensatory-based fuzzy reasoning method with the traditional Takagi–Sugeno–Kang (TSK) fuzzy model. The compensatory-based fuzzy reasoning method uses adaptive fuzzy operations of neuro-fuzzy systems that can make the fuzzy logic system more adaptive and effective. Layer 2 of the QNFC model contains quantum membership functions, which are multilevel activation functions. Each quantum membership function is composed of the sum of sigmoid functions shifted by quantum intervals. A self-constructing learning algorithm, which consists of the self-clustering algorithm (SCA), quantum fuzzy entropy and the backpropagation algorithm, is also proposed. The proposed SCA method is a fast, one-pass algorithm that dynamically estimates the number of clusters in an input data space. Quantum fuzzy entropy is employed to evaluate the information on pattern distribution in the pattern space. With this information, we can determine the number of quantum levels. The backpropagation algorithm is used to tune the adjustable parameters. The simulation results have shown that (1) the QNFC model converges quickly; (2) the QNFC model has a higher correct classification rate than other models.     

Classification of underground pipe scanned images using featureextraction and neuro-fuzzy algorithm

Pipeline surface defects such as holes and cracks cause major problems for utility managers, particularly when the pipeline is buried under the ground. Manual inspection for surface defects in the pipeline has a number of drawbacks, including subjectivity, varying standards, and high costs. Automatic inspection system using image processing and artificial intelligence techniques can overcome many of these disadvantages and offer utility managers an opportunity to significantly improve quality and reduce costs. A recognition and classification of pipe cracks using images analysis and neuro-fuzzy algorithm is proposed. In the preprocessing step the scanned images of pipe are analyzed and crack features are extracted. In the classification step the neuro-fuzzy algorithm is developed that employs a fuzzy membership function and error backpropagation algorithm. The idea behind the proposed approach is that the fuzzy membership function will absorb variation of feature values and the backpropagation network, with its learning ability, will show good classification efficiency.     

Optimizing fuzzy neural networks for tuning PID controllers using an orthogonal simulated annealing algorithm OSA

In this paper, we formulate an optimization problem of establishing a fuzzy neural network model (FNNM) for efficiently tuning proportional-integral-derivative (PID) controllers of various test plants with under-damped responses using a large number P of training plants such that the mean tracking error J of the obtained P control systems is minimized. The FNNM consists of four fuzzy neural networks (FNNs) where each FNN models one of controller parameters (K, T/sub i/, T/sub d/, and b) of PID controllers. An existing indirect, two-stage approach used a dominant pole assignment method with P=198 to find the corresponding PID controllers. Consequently, an adaptive neuro-fuzzy inference system (ANFIS) is used to independently train the four individual FNNs using input the selected 176 of the 198 PID controllers that 22 controllers with parameters having large variation are abandoned. The innovation of the proposed approach is to directly and simultaneously optimize the four FNNs by using a novel orthogonal simulated annealing algorithm (OSA). High performance of the OSA-based approach arises from that OSA can effectively optimize lots of parameters of the FNNM to minimize J. It is shown that the OSA-based FNNM with P=176 can improve the ANFIS-based FNNM in averagely decreasing 13.08% error J and 88.07% tracking error of the 22 test plants by refining the solution of the ANFIS-based method. Furthermore, the OSA-based FNNMs using P=198 and 396 from an extensive tuning domain have similar good performance with that using P=176 in terms of J.     

Three new fuzzy neural networks learning algorithms based on clustering, training error and genetic algorithm

Three new learning algorithms for Takagi-Sugeno-Kang fuzzy system based on training error and genetic algorithm are proposed. The first two algorithms are consisted of two phases. In the first phase, the initial structure of neuro-fuzzy network is created by estimating the optimum points of training data in input-output space using KNN (for the first algorithm) and Mean-Shift methods (for the second algorithm) and keeps adding new neurons based on an error-based algorithm. Then in the second phase, redundant neurons are recognized and removed using a genetic algorithm. The third algorithm then builds the network in one phase using a modified version of error algorithm used in the first two methods. The KNN method is shown to be invariant to parameter K in KNN algorithm and in two simulated examples outperforms other neuro-fuzzy approaches in both performance and network compactness.     

基于UKF的自适应模糊推理神经网络

如何生成最优的模糊规则数及模糊规则的自动生成和修剪是模糊神经网络训练算法研究的重点。针对这一问题,本文提出了基于UKF的自适应模糊推理神经网络(UKF-ANFIS)。首先,通过减法聚类确定UKF-ANFIS的模糊规则及其高斯隶属函数的中心和宽度参数;其次,分析了模糊神经网络的非线性动力系统表示,并用LLS和UKF分别学习线性和非线性的参数;然后,用误差下降率方法作为模糊规则修剪的策略,删除作用不大的规则;最后,通过典型的函数逼近和系统辨识实例,表明本文算法得到的模糊神经网络的结构更为紧凑,泛化性能也更佳。