基于修剪策略的D-FNN直接逆控制算法研究

神经网络是模拟人脑结构,它具有大规模并行及分布式信息处理能力,但是不能处理和描述模糊信息.模糊系统具有推理过程容易理解,但它很难实现自适应学习的功能.如果结合神经网络与模糊系统,可以取长补短.基于此,本文提出了一种新型动态模糊神经网络（Dynamic fuzzy neural network,D-FNN）学习算法.因为它具有结构和参数同时调整且学习速度快等优点,所以既可以在模糊逻辑系统中包含低级的神经网络学习和计算功能,也可以为神经网络提供高级的类似人的思维和推理的模糊逻辑系统.此外,本文还开发了生物医学工程应用算法程序,针对药物注射系统的直接逆控制案例进行了仿真,结果表明:D-FNN具有实时学习和控制能力强、参数估计和结构辨识同时进行等优点.     

D-FNN based soft-sensor modeling and migration reconfiguration of polymerizing process

A soft-sensor modeling method based on dynamic fuzzy neural network (D-FNN) is proposed for forecasting the key technology indicator convention velocity of vinyl chloride monomer (VCM) in the polyvinylchloride (PVC) polymerizing process. Based on the problem complexity and precision demand, D-FNN model can be constructed combining the system prior knowledge. Firstly, kernel principal component analysis (KPCA) method is adopted to select the auxiliary variables of soft-sensing model in order to reduce the model dimensionality. Then a hybrid structure and parameters learning algorithm of D-FNN is proposed to achieve the favorable approximation performance, which includes the rule extraction principles, the classification learning strategy, the precedent parameters arrangements, the rule trimming technology based on error descendent ratio and the consequent parameters decision based on extended Kalman filter (EKF). The proposed soft-sensor model can automatically determine if the fuzzy rules are generated/eliminated or not so as to realize the nonlinear mapping between input and output variables of the discussed soft-sensor model. Model migration method is adopted to realize the on-line adaptive revision and reconfiguration of soft-sensor model. In the end, simulation results show that the proposed model can significantly enhance the predictive accuracy and robustness of the technical-and-economic indexes and satisfy the real-time control requirements of PVC polymerizing production process.     

Research on prediction of traffic flow based on dynamic fuzzy neural networks

Combining the advantages of the neural network and fuzzy system, this paper makes a further research on the dynamic fuzzy neural networks (D-FNN) traffic flow prediction. Instead of being in consistence with growth of the input number, the fuzzy rule number of the D-FNN increases exponentially in the whole training network structure. In particular, this method can establish a required network structure automatically. This method is applied to the traffic flow time series to analyze and compare the predicting performance of the predicting model based on the neural network method and the adaptive neural fuzzy inference system by combining with the chaos theory. The simulation result shows that this method is quite effective and can improve the predicting accuracy.     

基于参数调整的动态模糊神经网络算法

模糊逻辑与神经网络结合形成的模糊神经网络同时具有易于表达人类知识、存储与学习分布信息的优点,基于此,提出一种基于参数调整的动态模糊神经网络算法。采用扩展卡尔曼滤波器法将全局算法划分为线性和非线性部分,线性参数由最小二乘法和滤波器法决定,非线性参数由训练样本和启发式法直接决定,线性和非线性参数可进行实时更新。仿真结果表明,该算法能保证更简洁的结构和更短的学习时间。     

Real-coded chaotic quantum-inspired genetic algorithm for training of fuzzy neural networks

In this paper, a novel approach to adjusting the weightings of fuzzy neural networks using a Real-coded Chaotic Quantum-inspired genetic Algorithm (RCQGA) is proposed. Fuzzy neural networks are traditionally trained by using gradient-based methods, which may fall into local minimum during the learning process. To overcome the problems encountered by the conventional learning methods, RCQGA algorithms are adopted because of their capabilities of directed random search for global optimization. It is well known, however, that the searching speed of the conventional quantum genetic algorithms (QGA) is not satisfactory. In this paper, a real-coded chaotic quantum-inspired genetic algorithm (RCQGA) is proposed based on the chaotic and coherent characters of Q-bits. In this algorithm, real chromosomes are inversely mapped to Q-bits in the solution space. Q-bits probability-guided real cross and chaos mutation are applied to the evolution and searching of real chromosomes. Chromosomes consisting of the weightings of the fuzzy neural network are coded as an adjustable vector with real number components that are searched by the RCQGA. Simulation results have shown that faster convergence of the evolution process in searching for an optimal fuzzy neural network can be achieved. Examples of nonlinear functions approximated by using the fuzzy neural network via the RCQGA are demonstrated to illustrate the effectiveness of the proposed method.     

Design of an adaptive self-organizing fuzzy neural network controller for uncertain nonlinear chaotic systems

Though the control performances of the fuzzy neural network controller are acceptable in many previous published papers, the applications are only parameter learning in which the parameters of fuzzy rules are adjusted but the number of fuzzy rules should be determined by some trials. In this paper, a Takagi–Sugeno-Kang (TSK)-type self-organizing fuzzy neural network (TSK-SOFNN) is studied. The learning algorithm of the proposed TSK-SOFNN not only automatically generates and prunes the fuzzy rules of TSK-SOFNN but also adjusts the parameters of existing fuzzy rules in TSK-SOFNN. Then, an adaptive self-organizing fuzzy neural network controller (ASOFNNC) system composed of a neural controller and a smooth compensator is proposed. The neural controller using the TSK-SOFNN is designed to approximate an ideal controller, and the smooth compensator is designed to dispel the approximation error between the ideal controller and the neural controller. Moreover, a proportional-integral (PI) type parameter tuning mechanism is derived based on the Lyapunov stability theory, thus not only the system stability can be achieved but also the convergence of tracking error can be speeded up. Finally, the proposed ASOFNNC system is applied to a chaotic system. The simulation results verify the system stabilization, favorable tracking performance, and no chattering phenomena can be achieved using the proposed ASOFNNC system.     

模糊神经网络在时间序列预测中的应用

文中提出了将模糊聚类与梯度算法相结合的一种改进的训练模糊神经网络的混合型算法。模拟结果表明，模糊神经网络可以成功地用于时间序列的预测，模糊神经网络的训练速度与模拟精度都优于传统多层ＢＰ网络。     

Adaptive noise cancellation using enhanced dynamic fuzzy neural networks

In this paper, a novel adaptive noise cancellation algorithm using enhanced dynamic fuzzy neural networks (EDFNNs) is described. In the proposed algorithm, termed EDFNN learning algorithm, the number of radial basis function (RBF) neurons (fuzzy rules) and input-output space clustering is adaptively determined. Furthermore, the structure of the system and the parameters of the corresponding RBF units are trained online automatically and relatively rapid adaptation is attained. By virtue of the self-organizing mapping (SOM) and the recursive least square error (RLSE) estimator techniques, the proposed algorithm is suitable for real-time applications. Results of simulation studies using different noise sources and noise passage dynamics show that superior performance can be achieved.     

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     

A fuzzy ensemble of parallel polynomial neural networks with information granules formed by fuzzy clustering

In this paper, we introduce a new category of fuzzy models called a fuzzy ensemble of parallel polynomial neural network (FEP²N²), which consist of a series of polynomial neural networks weighted by activation levels of information granules formed with the use of fuzzy clustering. The two underlying design mechanisms of the proposed networks rely on information granules resulting from the use of fuzzy C-means clustering (FCM) and take advantage of polynomial neural networks (PNNs).The resulting model comes in the form of parallel polynomial neural networks. In the design procedure, in order to estimate the optimal values of the coefficients of polynomial neural networks we use a weighted least square estimation algorithm. We incorporate various types of structures as the consequent part of the fuzzy model when using the learning algorithm. Among the diverse structures being available, we consider polynomial neural networks, which exhibit highly nonlinear characteristics when being viewed as local learning models.We use FCM to form information granules and to overcome the high dimensionality problem. We adopt PNNs to find the optimal local models, which can describe the relationship between the input variables and output variable within some local region of the input space.We show that the generalization capabilities as well as the approximation abilities of the proposed model are improved as a result of using polynomial neural networks. The performance of the network is quantified through experimentation in which we use a number of benchmarks already exploited within the realm of fuzzy or neurofuzzy modeling.     

一种基于RBF网络提取模糊规则的算法实现

径向基函数网络和模糊推理系统在一些柔和的情况下具有等价的功能,因此可以利用神经网络的学习算法来调节模糊系统的参数,学习后的模糊系统具有自学习和自组织性,但是削弱了模糊系统的可解释性。将模糊逻辑推理与神经网络控制技术相结合,分析了一种改进的径向基函数(RBF)神经网络结构,这种模糊神经网络结构能够有效地表达模糊系统可解释性这一突出特点,也使模糊系统具有了较好的自学习和自组织能力、通过VC 实现了基于这种RBF网络结构提取模糊规则的算法,并进行了仿真实验,仿真结果表明该算法是比较有效的。     

一种新的神经网络结构及其学习算法

提出了一个基于模糊集理论的新的神经网络结构及其学习算法。这种神经网络是对标准的BP网络修改后构成的(记作FIBP)。并通过几个实例仿真验证其有效性。实验结果表明，当用FIBP网络解决动态的、高度非线性的函数逼近时，其学习速度比BP网络快，精度高而且泛化能力高。     

On the optimal design of fuzzy neural networks with robust learningfor function approximation

A novel robust learning algorithm for optimizing fuzzy neural networks is proposed to address two important issues: how to reduce the outlier effects and how to optimize fuzzy neural networks, in the function approximation. This algorithm is able to reduce the outlier effects by cooperating with a conventional robust approach, and then to optimize fuzzy neural networks by determining the optimal learning rates which can minimize the next-step mean error at each iteration of our algorithm.     

Numerical solution of a system of fuzzy polynomials by fuzzy neural network

In this paper, a new approach for solving systems of fuzzy polynomials based on fuzzy neural network (FNN) is presented. This method can also lead to improve numerical methods. In this work, an architecture of fuzzy neural networks is also proposed to find a real root of a system of fuzzy polynomials (if exists) by introducing a learning algorithm. Finally, we illustrate our approach by numerical examples.     

Evolution computation based learning algorithms of polygonal fuzzy neural networks

We present two fuzzy conjugate gradient learning algorithms based on evolutionary algorithms for polygonal fuzzy neural networks (PFNN). First, we design a new algorithm, fuzzy conjugate algorithm based on genetic algorithm (GA). In the algorithm, we obtain an optimal learning constant η by GA and the experiment indicates the new algorithm always converges. Because the algorithm based on GA is a little slow in every iteration step, we propose to get the learning constant η by quantum genetic algorithm (QGA) in place of GA to decrease time spent in every iteration step. The PFNN tuned by the proposed learning algorithm is applied to approximation realization of fuzzy inference rules, and some experiments demonstrate the whole process. © 2011 Wiley Periodicals, Inc.     

基于D-FNN的开关磁阻无位置传感器的研究

提出了一种基于扩展径向基函数(RBF)神经网络的动态模糊神经网络(D-FNN)的开关磁阻电机无位置传感器控制的新方法。动态模糊神经网络系统以在线采样的相绕组的电流和磁链为输入,以转子位置角度为输出,从而建立起电流和磁链、转子位置角度的非线性映射关系;训练完成后,用D-FNN输出结果取代位置传感器角度信号,实现电机无位置传感器运行。仿真和实验结果表明:由D-FNN获得的角度信号和由位置传感器获得的角度信号相比误差小,电机能够准确换相,且输出转矩波动小,转速曲线平滑,电机在无位置传感器下运行良好。     

A fast and accurate online self-organizing scheme for parsimonious fuzzy neural networks

In this paper, we present a fast and accurate online self-organizing scheme for parsimonious fuzzy neural networks (FAOS-PFNN), where a novel structure learning algorithm incorporating a pruning strategy into new growth criteria is developed. The proposed growing procedure without pruning not only speeds up the online learning process but also facilitates a more parsimonious fuzzy neural network while achieving comparable performance and accuracy by virtue of the growing and pruning strategy. The FAOS-PFNN starts with no hidden neurons and parsimoniously generates new hidden units according to the proposed growth criteria as learning proceeds. In the parameter learning phase, all the free parameters of hidden units, regardless of whether they are newly created or originally existing, are updated by the extended Kalman filter (EKF) method. The effectiveness and superiority of the FAOS-PFNN paradigm is compared with other popular approaches like resource allocation network (RAN), RAN via the extended Kalman filter (RANEKF), minimal resource allocation network (MRAN), adaptive-network-based fuzzy inference system (ANFIS), orthogonal least squares (OLS), RBF-AFS, dynamic fuzzy neural networks (DFNN), generalized DFNN (GDFNN), generalized GAP-RBF (GGAP-RBF), online sequential extreme learning machine (OS-ELM) and self-organizing fuzzy neural network (SOFNN) on various benchmark problems in the areas of function approximation, nonlinear dynamic system identification, chaotic time-series prediction and real-world regression problems. Simulation results demonstrate that the proposed FAOS-PFNN algorithm can achieve faster learning speed and more compact network structure with comparably high accuracy of approximation and generalization.     

基于模糊神经网络水下机器人直接自适应控制

提出了基于广义动态模糊神经网络的水下机器人直接自适应控制方法, 该控制方法既不需要预先知道模糊神经结构, 也不需要预先的训练阶段, 完全通过在线自适应学习算法构建水下机器人的逆动力学模型. 首先, 本文提出了基于这种网络结构的水下机器人直接自适应控制器, 然后, 利用 Lyapunov 稳定理论, 证明了基于该控制器的水下机器人控制系统闭环稳定性, 最后, 采用某水下机器人模型仿真验证了该控制方法的有效性.     

基于模糊神经元网络的智能车辆个性自动驾驶系统的设计与实现

介绍了一种利用模糊神经元网络实现车辆自动驾驶的设计方案.其基本设计思想 是首先通过模糊逻辑描述驾驶者的驾驶行为,然后利用驾驶者实际驾驶时采集的车辆运行情 况作为训练数据,通过神经元网络的自学习功能修改和改进模糊控制所需的输入/输出信 号的隶属度函数以及模糊推理的运算关系,做到简单控制实现与复杂学习算法的有效结合, 从而实现模糊神经元控制.本方案为智能车辆实现个性化自主或辅助自动驾驶提供了一种非 常有效的机制.     

具有学习功能的多值模糊神经网络控制系统

对于具有非线性、大时滞、不确定性等特性的难以用精确数学模型描述的多变量复杂系统,靠传统控制理论难以获得理想的控制效果。基于模糊神经网络控制技术不依赖于被控对象精确的数学模型,且能根据被控对象参数的变化自适应调节控制规则和隶属函数参数的特性,进行了采用模糊神经网络控制器实现其控制的应用研究。采用典型的前向型模糊神经网络模型,给出了具有学习功能的多值模糊神经网络控制系统的一种设计方法。仿真实验证明,该系统能够获得较理想的控制效果。