具有快速学习算法的补偿模糊神经网络的故障诊断

在融合了模糊逻辑的推理能力和神经网络的自适应、自学习能力。同时采用输出空间模式聚类的快速学习算法,引入补偿模糊神经元,模糊运算采用动态的全局优化运算,使学习后的网络具有更高的容错性,并弥补了神经网络学习耗时的缺点,提高了效率,进行了故障检测的仿真分析,并且将其运用于未建模系统的故障诊断中取得良好的效果。     

An efficient compensatory neuro-fuzzy system and its applications

In this study, a compensatory neuro-fuzzy system (CNFS) is proposed. The compensatory fuzzy reasoning method uses adaptive fuzzy operations of a neuro-fuzzy system to make the fuzzy logic system more adaptive and effective. Furthermore, an online learning algorithm that consists of structure learning and parameter learning is proposed to automatically construct the CNFS. The structure learning is based on the fuzzy similarity measure to determine the number of fuzzy rules, and the parameter learning is based on backpropagation algorithm to adjust the parameters. The simulation results have shown that (1) the CNFS model converges quickly and (2) the CNFS model has a lower root mean square (RMS) error than other models.     

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.     

基于自适应神经元学习模糊控制规则

本文给出了利用自适应神经元学习、修改模糊控制规划的新方法，该方法可以学习与当前控制过程输出性能有关的在过去起作用的控制规划，可以随过程环境变化自动调整控制规划，以改善过程输出性能。     

Adaptive control of bilateral teleoperation system with compensatory neural-fuzzy controllers

This paper presents two adaptive neural-fuzzy controllers equipped with compensatory fuzzy control in order to adjust membership functions, and as well to optimize the adaptive reasoning by using a compensatory learning algorithm. To the first controller is applied compensatory neural-fuzzy inference (CNFI) and to the second compensatory adaptive neural fuzzy inference system (CANFIS). Each controller is incorporated into a two channel bilateral teleoperation architecture involving force-position scheme, which combines the position control of the slave system with force reflection on the master. An analysis of stability and transparency based on a passivity framework is carried out. The resulting controllers are implemented on a one degree of freedom teleoperation system actuated by DC motors. The experimental results obtained show a fairly high accuracy in terms of position and force tracking, under free space motion and hard contact motion, what highlights the effectiveness of the proposed controllers.

     

A self-learning fuzzy logic controller using genetic algorithmswith reinforcements

This paper presents a new method for learning a fuzzy logic controller automatically. A reinforcement learning technique is applied to a multilayer neural network model of a fuzzy logic controller. The proposed self-learning fuzzy logic control that uses the genetic algorithm through reinforcement learning architecture, called a genetic reinforcement fuzzy logic controller, can also learn fuzzy logic control rules even when only weak information such as a binary target of “success” or “failure” signal is available. In this paper, the adaptive heuristic critic algorithm of Barto et al. (1987) is extended to include a priori control knowledge of human operators. It is shown that the system can solve more concretely a fairly difficult control learning problem. Also demonstrated is the feasibility of the method when applied to a cart-pole balancing problem via digital simulations     

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.     

自适应神经模糊推理结合PID控制的并联机器人控制方法

针对6自由度液压驱动并联机器人的精确控制问题,提出一种结合自适应神经模糊推理系统(ANFIS)和比例积分微分(PID)控制的机器人控制方法。首先,利用浮动坐标系描述法(FFRF)来模拟机器人柔性组件,并构建并联机器人的拉格朗日动力学模型。然后,根据模糊推理中的模糊规则来自适应调整PID控制器参数。最后,利用神经自适应学习算法使模糊逻辑能计算隶属度函数参数,从而使模糊推理系统能追踪给定的输入和输出数据。将该控制器与传统PID控制器、模糊PID控制器进行比较,结果表明,ANFIS自整定PID控制器大大减小了末端器位移误差,能很好的控制并联机器人末端机械手的运动。     

自适应模糊逻辑系统的聚类学习算法研究

基于模糊逻辑系统一般数学模型，利用最近邻聚类学习算法对样本数据进行自适应分组，并对系统进行训练，从而使模糊逻辑系统具有自学习功能。     

COMPRESSION AND EXPANSION OF FUZZY RULE BASES BY USING CRISP-FUZZY NEURAL NETWORKS

A fuzzy neural network with knowledge discovery FNNKD is designed to perform adaptive compensatory fuzzy reasoning based on more useful and more heuristic primary fuzzy sets. In order to overcome the weakness of the conventional crisp neural network and the fuzzy operation oriented neural network, we have developed a general fuzzy reasoning oriented fuzzy neural network called a crisp-fuzzy neural network CFNN that is capable of extracting high-level knowledge such as fuzzy IF-THEN rules from either crisp data or fuzzy data. A CFNN can effectively compress a 5 5 fuzzy IF-THEN rule base of a cart-pole balancing system to a 3 3 one, then to a 2 2 one, and finally to a 1 1 one, and can expand on invalid sparse 3 3 fuzzy IF-THEN rule base of a cart-pole balancing system to a valid 5 5 one. In addition, a CFNN can control a more complex cart-pole balancing system with random fuzzy noise inputs and outputs i.e., nonconventional using crisp inputs and outputs without any noise . The simulations have indicated that a CFNN is an efficient neurofuzzy system with abilities to discover new fuzzy knowledge from either numerical data or fuzzy data, compress and expand fuzzy knowledge, and do fuzzy reasoning.     

Direct adaptive interval type-2 fuzzy control of multivariable nonlinear systems

A fuzzy logic controller equipped with a training algorithm is developed such that the H^∞ tracking performance should be satisfied for a model-free nonlinear multiple-input multiple-output (MIMO) system, with external disturbances. Due to universal approximation theorem, fuzzy control provides nonlinear controller, i.e., fuzzy logic controllers, to perform the unknown nonlinear control actions and the tracking error, because of the matching error and external disturbance is attenuated to arbitrary desired level by using H^∞ tracking design technique. In this paper, a new direct adaptive interval type-2 fuzzy controller is developed to handle the training data corrupted by noise or rule uncertainties for nonlinear MIMO systems involving external disturbances. Therefore, linguistic fuzzy control rules can be directly incorporated into the controller and combine the H^∞ attenuation technique. Simulation results show that the interval type-2 fuzzy logic system can handle unpredicted internal disturbance, data uncertainties, very well, but the adaptive type-1 fuzzy controller must spend more control effort in order to deal with noisy training data. Furthermore, the adaptive interval type-2 fuzzy controller can perform successful control and guarantee the global stability of the resulting closed-loop system and the tracking performance can be achieved.     

模糊神经网络算法在倒立摆控制中的应用

本文利用一种可以进行结构和参数学习的模糊神经网络成功地控制一级倒立摆,该网络是一种多层前馈网络,它将传统模糊控制器的基本要件综合到网络结构中。从而使该网络既具备神经网络的低级学习能力,从而还具备模糊逻辑系统类似人的高级推理能力。因而,给定训练数据后,该网络不仅可以学习网络参数,同时还可以学习网络结构。结构学习确定了表示了模糊规则和模糊分段数的连接类型以及隐节点数目。对一级倒立摆的实际控制效果可以证明该算法的性能和实用性。     

Adaptive fuzzy spiking neural P systems for fuzzy inference and learning

Fuzzy spiking neural P systems (in short, FSN P systems) are a novel class of distributed parallel computing models, which can model fuzzy production rules and apply their dynamic firing mechanism to achieve fuzzy reasoning. However, these systems lack adaptive/learning ability. Addressing this problem, a class of FSN P systems are proposed by introducing some new features, called adaptive fuzzy spiking neural P systems (in short, AFSN P systems). AFSN P systems not only can model weighted fuzzy production rules in fuzzy knowledge base but also can perform dynamically fuzzy reasoning. It is important to note that AFSN P systems have learning ability like neural networks. Based on neuron's firing mechanisms, a fuzzy reasoning algorithm and a learning algorithm are developed. Moreover, an example is included to illustrate the learning ability of AFSN P systems.     

An effective hybrid approach of desirability,fuzzy logic,ANFIS and LAPO algorithm for optimizing compliant mechanism

This paper proposes an efficient hybrid approach for solving multi-objective optimization design of a compliant mechanism. The approach is developed by integrating desirability function approach, fuzzy logic system, adaptive neuro-fuzzy inference system, and Lightning attachment procedure optimization. Box–Behnken design is used to form a numerically experimental matrix. First, a refinement of design variables is conducted through analysis of variance and Taguchi approach in terms of considerably eliminating space of design variables and computation efforts. Next, desirability of two objective functions is computed and transferred into the fuzzy logic system. The output of fuzzy logic system is regarded as single combined objective function. Subsequently, a modeling for fuzzy output is developed via adaptive neuro-fuzzy inference system. Then, LAPO algorithm is adopted for solving the optimization problem. By investigating three different numerical examples, performance of the proposed approach is validated. Numerical results revealed that the proposed approach has a computational accuracy better than that of Taguchi-based fuzzy logic reasoning. Finally, case study 1 is chosen as an optimal solution for the mechanism. Furthermore, the effectiveness of proposed approach is greater than that of the Jaya algorithm and TLBO algorithm through Wilcoxon signed rank test and Friedman test. The proposed approach can be used for related engineering fields.

     

SoHyFIS-Yager: A self-organizing Yager based Hybrid neural Fuzzy Inference System

The Hybrid neural Fuzzy Inference System (HyFIS) is a multilayer adaptive neural fuzzy system for building and optimizing fuzzy models using neural networks. In this paper, the fuzzy Yager inference scheme, which is able to emulate the human deductive reasoning logic, is integrated into the HyFIS model to provide it with a firm and intuitive logical reasoning and decision-making framework. In addition, a self-organizing gaussian Discrete Incremental Clustering (gDIC) technique is implemented in the network to automatically form fuzzy sets in the fuzzification phase. This clustering technique is no longer limited by the need to have prior knowledge about the number of clusters present in each input and output dimensions. The proposed self-organizing Yager based Hybrid neural Fuzzy Inference System (SoHyFIS-Yager) introduces the learning power of neural networks to fuzzy logic systems, while providing linguistic explanations of the fuzzy logic systems to the connectionist networks. Extensive simulations were conducted using the proposed model and its performance demonstrates its superiority as an effective neuro-fuzzy modeling technique.     

高精度RBP-模糊推理复合学习系统

该文提出了高精度RBP-模糊推理复合学习系统.系统主要由基于鲁棒估计的鲁棒BP 学习环节和基于混合合成推理的模糊推理环节构成.该学习系统的主要特点是可由鲁棒BP 算法和min-max,max-min模糊推理算法简单地实现.最后通过在目标跟踪问题中应用结 果,表示了该算法的高精度和鲁棒性.     

高精度RBP－模糊推理复合学习系统

该文提出了高精度ＲＢＰ－模糊推理复合学习系统．系统主要由基于鲁棒估计的鲁棒ＢＰ学习环节和基于混合合成推理的模糊推理环节构成．该学习系统的主要特点是可由鲁棒ＢＰ算法和ｍｉｎ－ｍａｘ，ｍａｘ－ｍｉｎ模糊推理算法简单地实现．最后通过在目标跟踪问题中应用结果，表示了该算法的高精度和鲁棒性．     

Effective learning system techniques for human–robot interaction in service environment

HRI (Human–Robot Interaction) is often frequent and intense in assistive service environment and it is known that realizing human-friendly interaction is a very difficult task because of human presence as a subsystem of the interaction process. After briefly discussing typical HRI models and characteristics of human, we point out that learning aspect would play an important role for designing the interaction process of the human-in-the loop system. We then show that the soft computing toolbox approach, especially with fuzzy set-based learning techniques, can be effectively adopted for modeling human behavior patterns as well as for processing human bio-signals including facial expressions, hand/ body gestures, EMG and so forth. Two project works are briefly described to illustrate how the fuzzy logic-based learning techniques and the soft computing toolbox approach are successfully applied for human-friendly HRI systems. Next, we observe that probabilistic fuzzy rules can handle inconsistent data patterns originated from human, and show that combination of fuzzy logic, fuzzy clustering, and probabilistic reasoning in a single frame leads to an algorithm of iterative fuzzy clustering with supervision. Further, we discuss a possibility of using the algorithm for inductively constructing probabilistic fuzzy rule base in a learning system of a smart home. Finally, we propose a life-long learning system architecture for the HRI type of human-in-the-loop systems.     

An ART-based fuzzy adaptive learning control network

This paper addresses the structure and an associated online learning algorithm of a feedforward multilayer neural net for realizing the basic elements and functions of a fuzzy controller. The proposed fuzzy adaptive learning control network (FALCON) can be contrasted with traditional fuzzy control systems in network structure and learning ability. An online structure/parameter learning algorithm, FALCON-ART, is proposed for constructing FALCON dynamically. It combines backpropagation for parameter learning and fuzzy ART for structure learning. FALCON-ART partitions the input state space and output control space using irregular fuzzy hyperboxes according to the data distribution. In many existing fuzzy or neural fuzzy control systems, the input and output spaces are always partitioned into “grids”. As the number of variables increases, the number of partitioned grids grows combinatorially. To avoid this problem in some complex systems, FALCON-ART partitions the I/O spaces flexibly based on data distribution. It can create and train FALCON in a highly autonomous way. In its initial form, there is no membership function, fuzzy partition, and fuzzy logic rule. They are created and begin to grow as the first training pattern arrives. Thus, the users need not give it any a priori knowledge or initial information. FALCON-ART can online partition the I/O spaces, tune membership functions, find proper fuzzy logic rules, and annihilate redundant rules dynamically upon receiving online data