Adaptive TS‐FNN control for a class of uncertain multi‐time‐delay systems: The exponentially stable sliding mode‐based approach

This paper presents an adaptive Takagi–Sugeno fuzzy neural network (TS‐FNN) control for a class of multiple time‐delay uncertain nonlinear systems. First, we develop a sliding surface guaranteed to achieve exponential stability while considering mismatched uncertainty and unknown delays. This exponential stability result based on a novel Lyapunov–Krasovskii method is an improvement when compared with traditional schemes where only asymptotic stability is achieved. The stability analysis is transformed into a linear matrix inequalities problem independent of time delays. Then, a sliding mode control‐based TS‐FNN control scheme is proposed to achieve asymptotic stability for the controlled system. Since the TS‐FNN combines TS fuzzy rules and a neural network structure, fewer numbers of fuzzy rules and tuning parameters are used compared with the traditional pure TS fuzzy approach. Moreover, all the fuzzy membership functions are tuned on‐line even in the presence of input uncertainty. Finally, simulation results show the control performance of the proposed scheme. Copyright © 2008 John Wiley & Sons, Ltd.     

Compact self‐constructing recurrent fuzzy neural network with decision feedback for quadrature amplitude modulation signaling systems

This paper proposes a novel adaptive decision feedback equalizer (DFE) based on compact self‐constructing recurrent fuzzy neural network (CSRFNN) for quadrature amplitude modulation systems. Without the prior knowledge of channel characteristics, a novel training scheme containing both compact self‐constructing learning (CSL) and real‐time recurrent learning algorithms is derived for the CSRFNN. The proposed CSL algorithm adopts two evaluation criteria to intelligently decide the number of fuzzy rules that are necessary. The real‐time recurrent learning is performed simultaneously with the CSL at each time instant to adjust DFE parameters. The proposed DFE is compared with several neural network‐based DFEs on a nonlinear complex‐valued channel. The results show that the CSRFNN DFE is superior to classical neural network DFEs in terms of symbol‐error rate, convergence speed, and time cost. Copyright © 2011 John Wiley & Sons, Ltd.     

Hybrid chaos search genetic algorithm and meta-heuristics method for short-term load forecasting

A hybrid chaos search genetic algorithm (CGA) /fuzzy system (FS), simulated annealing (SA) and neural fuzzy network (NFN) method for load forecasting is presented in this paper. A fuzzy hyper-rectangular composite neural networks (FHRCNNs) was used for the initial load forecasting. Then, we used CGAFS and SA to find the optimal solution of the parameters of the FHRCNNs, instead of back-propagation (BP) (including parameters such as synaptic weights, biases, membership functions, sensitivity factor in membership functions and adjustable synaptic weights). First, the CGAFS generates a set of feasible solution parameters and then puts the solution into the SA. The CGAFS has good global optimal search capabilities, but poor local optimal search capabilities. The SA method on the other hand has good local optimal search capabilities. We combined both methods to try and obtain both advantages, and in doing so eliminate the drawback of the traditional artificial neural networks (ANN) training by BP (where the weights and biases are always trapped into a local optimum, which then leads the solution to sub-optimization). Finally, we used the CGAFS and SA combined with NFN (CGAFSSA–NFN) to see if we could improve the quality of the solution, and if we actually could reduce the error of load forecasting. The proposed CGAFSSA–NFN load forecasting scheme was tested using the data obtained from a sample study, including 1 year, 1 week and 24-h time periods. The proposed scheme was then compared with ANN, evolutionary programming combined with ANN (EP–ANN), genetic algorithm combined with ANN (GA–ANN), and CGAFSSA–NFN. The results demonstrated the accuracy of the proposed load-forecasting scheme.     

High‐speed directional relaying using adaptive neuro‐fuzzy inference system and fundamental component of currents

This paper proposes an adaptive neuro‐fuzzy approach for fault direction estimation in sectional transmission lines. The ANFIS (adaptive neuro‐fuzzy inference system) network is designed by selecting different input and output member functions and rules for training and testing of fault cases. The fundamental component of current obtained from three‐phase current employing discrete Fourier transform (DFT) is given as input to the ANFIS module. The trained ANFIS module is then tested for detecting the fault direction. The relay is located at middle section‐2, which is considered as the primary section to be protected. It takes section‐1 as reverse section and section‐3 as forward section. This method is not affected by the variation of fault type, fault inception angle, fault location, and fault resistance. The biggest advantage of the ANFIS method is that it can detect the fault direction within 1 ms in almost all cases, which is much less than the implemented distance relaying scheme. The second advantage of the method is that it takes less number of training samples to detect the direction accurately as compared to other training algorithms like ANN, SVM, etc. The third advantage of the proposed scheme is that it offers protection to 99% of line length in all the three sections. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Q(λ)‐learning adaptive fuzzy logic controllers for pursuit–evasion differential games

This paper addresses the problem of tuning the input and the output parameters of a fuzzy logic controller. A novel technique that combines Q(λ)‐learning with function approximation (fuzzy inference system) is proposed. The system learns autonomously without supervision or a priori training data. The proposed technique is applied to three different pursuit–evasion differential games. The proposed technique is compared with the classical control strategy, Q(λ)‐learning only, and the technique proposed by Dai et al. (2005) in which a neural network is used as a function approximation for Q‐learning. Computer simulations show the usefulness of the proposed technique. Copyright © 2011 John Wiley & Sons, Ltd.     

基于模糊补偿的神经网络算法在调速系统中的应用

提出了一种带模糊补偿的神经网络算法并应用在异步电机速度控制系统中，一个动态神经网络用于被控装置的在线辨识，然后根据被控装置的输出和参考模型的响应迭代出控制信号，具有四条简单规则的模糊逻辑块用于提高整个系统的闭环特性。仿真结果显示，对比传统的最优PID控制器，本文提出的控制策略具有更好的瞬变特性及抗干扰特性。     

Enhanced adaptive control for a benchmark piezoelectric‐actuated system via fuzzy approximation

This paper investigates the problem of the high precision tracking control of piezoelectric actuators (PEAs) without using the inverse of the uncertain hysteresis. Based on fuzzy system approximator and particle swarm optimization (PSO) algorithm, a proposed enhanced adaptive controller is developed. The proposed controller provides fast and robust adaptation simultaneously with guaranteed desired transient performance. Moreover, it has a simple form and requires fewer adaptation parameters. The adaptation gain is determined via PSO algorithm. The proposed controller is tested on a lab‐scale PEA system. Experimental results with comparative studies with different techniques have been developed. The simulation results reveal that the proposed controller outperforms the other controllers in terms of normalized root‐mean‐square and maximum tracking errors for different frequencies.     

一种新型模糊神经网络自适应控制器

文章利用模糊神经网络的模糊推理能力以及前馈神经网络的逼近能力,将其与自适应控制方案结合,并取带有控制增量约束的广义目标函数作为优化指标;从而推导出一种能对非线性非最小相位系统进行有效控制的模糊神经网络间接自适应控制器。在网络学习算法上分别采用Davidon最小二乘法和带有动量项的BP算法。仿真结果表明了该方法的有效性。     

Neural‐hybrid control of systems with sandwiched dead‐zones

The control of systems that have sandwiched nonsmooth nonlinearities, such as a dead‐zone sandwiched between two dynamic blocks, is addressed. An adaptive inverse control scheme using a hybrid controller structure and a neural network based inverse compensator, is proposed for such systems with unknown sandwiched dead‐zone. This neural‐hybrid controller consists of an inner loop discrete‐time feedback structure incorporated with an adaptive inverse using a neural network for the unknown dead‐zone, and an outer‐loop continuous‐time feedback control law for achieving desired output tracking. The dead‐zone compensator consists of two neural networks, one used as an estimator of the sandwiched dead‐zone function and the other for the compensation itself. The compensator neural network has neurons that can approximate jump functions such as a dead‐zone inverse. The weights of the two neural networks are tuned using a modified gradient algorithm. Simulation results are given to illustrate the performance of the proposed neural‐hybrid controller. Copyright © 2002 John Wiley & Sons, Ltd.     

电液位置伺服系统的模糊神经网络控制

针对电液位置伺服系统中的非线性、参数时变性等复杂因素,设计了一种模糊神经网络控制方案。由于常规的模糊神经网络学习算法具有权值调整复杂,收敛速度慢的缺点,因此采用模糊逐级误差逼近方法来调整模糊神经网络的权值。该算法易于实现,网络权值在线学习速度较快,而且计算量小于其他的常规神经网络学习算法。将该方法应用于电液位置伺服控制系统中,在对象参数摄动情况下,进行了仿真研究。仿真结果表明,采用该方法所设计的控制器满足系统对快速性和稳态精确度的要求,系统的鲁棒性增强,验证了方法的有效性。     

Application of adaptive neuro‐fuzzy inference for wind power short‐term forecasting

The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. In this paper, an adaptive neuro‐fuzzy inference approach is proposed for short‐term wind power forecasting. Results from a real‐world case study are presented. A thorough comparison is carried out, taking into account the results obtained with other approaches. Numerical results are presented and conclusions are duly drawn. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

基于动态模糊神经网络的变桨距系统辨识

针对风力发电机组运行过程难以建立精确的数学模型的特点,将动态模糊神经网络应用于风力发电变桨距系统的辨识中.该模糊神经网络的结构基于扩展的径向基神经网络,在功能上等价于TSK模糊系统,其学习算法的最大特点是参数的调整和结构的辨识同时进行,且学习速度快.通过对风力发电变桨距系统中桨距角和风力机转速的非线性动态过程所进行的仿...     

基于模糊神经网络的二自由度内模控制

针对工业控制领域具有代表性的过程控制对象,设计了一种二自由度内模控制器,可以同时独立地调节日标值跟踪特性、干扰抑制特性和鲁棒性.并提出了一种基于模糊神经网络的二自由度内模控制参数在线智能整定方法.理论分析和仿真结果表明,所提出的方法设计简单、参数凋整方便,可以使系统同时具有良好的目标值跟踪特性、干扰抑制特性和鲁棒性.     

Modeling of non‐linear systems by FWNNs and their intelligent control

By utilizing some of the important properties of wavelets like denoising, compression, multiresolution along with the concepts of fuzzy logic and neural network, two fuzzy wavelet neural networks (FWNNs) are proposed for approximating any arbitrary non‐linear function, hence, identifying a non‐linear system. We have fuzzified the output of DWT block, which receives the given inputs, in the proposed two methods: one using compression property and other using multiresolution property. We present a new type of fuzzy neuron model, each non‐linear synapse of which is characterized by a set of fuzzy implication rules with singleton weights in their consequents. It is shown that noise and disturbance in the reference signal are reduced with wavelets and also the variation of somatic gain, the parameter that controls the slope of the activation function in the neural network, leads to more accurate output. Identification results are found to be accurate and speed of their convergence is fast. Next, we simulate a control system for keeping output at a desired level by using the identified models. Two self‐learning controllers are designed in this simulation. One is a self‐learning fuzzy PI controller and other is a NN controller. Simulation results show that the NN controller is more adaptive and fast. Copyright © 2005 John Wiley & Sons, Ltd.     

改进的TSK型动态模糊神经网络在短期负荷预测中的应用(英文)

将改进的TSK型模糊神经网络(fuzzy neural network,FNN)应用于短期负荷预测。该FNN由椭圆基函数构成神经元的中心和宽度参数,并且具有以下特征:网络结构和参数可自动并同时进行调整,不需提前分割输入空间,也不需提前选择网络初始参数;模糊规则在学习过程中可动态增删,不需采用迭代算法即可快速生成。这种模糊规则可动态增删的模糊神经网络(growing and pruning fuzzy neural network,GPFNN)简单有效,可以降低网络的复杂性,加快网络的学习速度。使用EUNITE竞赛数据作测试数据对上述GPFNN方法进行测试,结果表明采用该方法进行短期负荷预测时可获得较高的准确率。     

Filtered gradient active fuzzy neural network noise control in an enclosure backed by a clamped plate

The performance of conventional linear algorithms in active noise control applications deteriorates facing nonlinearities in the system mainly because of loudspeakers. On the other hand, fuzzy logic and neural networks are good candidates to overcome this drawback. In this paper, the acoustic attenuation of noise in a rectangular enclosure with a flexible panel and five rigid walls is presented both theoretically and experimentally using filtered gradient fuzzy neural network (FGFNN) error back propagation algorithm in which the secondary path effect is implemented in derivation of updating rules. Considering this effect in updating rules leads to faster convergence and stability of the active noise control system. On the other hand, the primary path in the investigated system comprises an identified nonlinear model of loudspeaker inside the aforementioned box, parameters of which vary with the input current. The loudspeaker is identified using series‐parallel neural network model identification method. As a comparison, the performance of filtered‐x least mean squares and FGFNN algorithms are compared. It is observed that FGFNN controller exhibits far better results in the presence of loudspeakers with nonlinear behavior in primary path.Copyright © 2012 John Wiley & Sons, Ltd.     

Design of fuzzy neural network‐based robust gain scheduling controllers

This paper proposes a method of robust gain scheduling control design by intelligent control that uses a fuzzy neural network without a model. We create a system that is robust and capable of automatic gain control against the conventional fixed PID control system, design a neural network which learns inverse dynamics as feedforward compensation, along with a two‐degree‐of‐freedom control to perform feedback compensation, produce a control system which adaptively adjusts the gain according to changes of the target errors, and verify the effectiveness of the proposed method. © 2009 Wiley Periodicals, Inc. Electr Eng Jpn, 169(4): 21–28, 2009; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20820     

带补偿的神经网络辩识器在异步电机调速系统中的应用

本文提出了一种带模糊补偿的神经网络算法并应用在异步电机速度控制系统中.一个动态神经网络用于被控装置的在线辨识,然后根据被控装置的输出和参考模型的响应迭代出控制信号,具有四条简单规则的模糊逻辑块用于提高整个系统的闭环特性.仿真结果显示,对比传统的最优PID控制器,本文提出的控制策略具有更好的瞬变特性及抗干扰特性.     

一类非线性系统的自组织模糊神经网络控制

针对一类MIMO不确定非线性有干扰且控制增益符号未知的系统进行跟踪控制的问题,提出了一种在线自组织模糊神经网络的改进算法,用以克服参数选择困难的问题,并基于该算法给出了一种自适应鲁棒控制方法。首先基于主导输入的概念将MIMO系统分解为多个SISO系统构成的系统,然后结合自组织模糊神经网络在线对系统中的未知函数进行逼近,对网络结构和参数实现在线调节,再利用Nussbaum函数来克服控制增益符号未知,并且引入鲁棒项及复合误差的估计来补偿复合误差。最后基于Lyapunov稳定性理论证明了整个闭环系统半全局一致最终有界。理论和仿真结果表明提出方法的有效性。