一种基于超盒表示的规则提取方法

提出一种从训练样本提取基于超盒表示的模糊规则的方法,用于模式分类.这种方法把模式空间划分成模糊超盒,作为模糊规则的前件,规则的后件是相应的类别名称,同时给出每一条模糊规则的置信度.模糊分类规则从训练样本通过学习算法提取.规则提取方法可以分为,对于单个训练模式进行规则前件和后件的局部在线学习,和对于全部训练模式进行循环学习.实验显示规则提取的过程,说明通过这种方法能够获得有效的模式分类规则.     

基于模糊神经网络的传感器可信度实时获取

针对传感器在复杂环境中所测信息不完全准确的问题,提出了一种基于专家规则的零阶Sugeno模糊模型神经网络来获取传感器可信度的方法.神经网络经训练样本训练后,可以根据传感器状态和环境信息实时地得到传感器可信度.将该模型学习算法中的最小二乘识别器加以改进,并引入了遗忘因子,可以使该网络实现在线学习,不断更新网络参数.仿真结果表明该模糊神经网络可以有效地获得传感器可信度,且越小则网络在线学习能力越强.     

具有模糊信息和自学习权重的分布式检测算法

本文研究了一种由局部自适应模糊检测器和在线自学习融合算法所构成的分布式信号检测系统的设计方法。由模糊集对不精确信号参数的局部检测器进行建模,该模糊模型可自适应不精确信号参数的变化。融合中心以最佳融合规则作为目标函数在线自学习局部判决的权重。局部模糊检测器的鲁棒性和自学习融合算法的自适应性使该分布式检测系统在不确定环境下的检测性能得到提高。也使该系统能够处理未知分布的未知参数以及非随机未知参数的分布     

基于局部线性度量的模糊建模

提出了一种高木-关野模糊逻辑系统的学习算法,该算法的核心是基于数据样本局部线性度量的聚类,它可以有效地确定规则数以及相应模糊逻辑系统的参数初值.通过对系统的参数进行优化,可以很好的描述输入输出变量间的关系.仿真实验说明了该方法的优越性.     

模糊概念格在知识发现的应用及一种构造算法

基于有限L_背景的模糊格在扩展和时空复杂度上有局限.本文定义了广义的模糊概念格和其上的截运算以简化格构造,提出了一种模糊格构造算法.在概念格结点级上定义了两个模糊参数α和 ,以避免提取因高偏差导致的无效规则.给出一个实例,说明了从模糊概念格提取不确定规则、计算规则支持度、置信度的原则、方法.实现了构造算法与Godin算法的对比实验,结果表明本算法在时空性能上要优于Godin算法.     

含有否定命题逻辑推理的一致性模糊Petri网模型

模糊Petri网(Fuzzy Petri Net,FPN)是Petri网(Petri Net,PN)的模糊化描述的一种扩充.基于FPN模型的模糊推理规则表示和模糊推理已经得到了广泛的研究.传统的方法不能准确表示含有否定命题的产生式规则,并解决正确推理问题.本文讨论了模糊逻辑中否定的含义,将条件命题中的否定理解为其对推理规则的阻碍作用,结果命题中的否定理解为规则中的前提条件阻碍该命题的发生.在此基础上提出一种新的适合于含有否定命题逻辑规则的一致性FPN模型(Consistent Fuzzy Petri Net,CFPN)表示方法,同时在CFPN模型中引入域值的概念,并给出相应的形式化推理算法及相关证明.     

基于模糊推理的图像放大算法

图像放大技术在许多图像应用系统中具有重要作用.图像的主要特性之一是具有模糊特性,将模糊处理方法引入图像放大算法中,提出一种基于模糊推理的图像放大算法.该算法首先建立了基于模糊推理的图像模型,假设图像相邻的像素点是一种特定的模糊推理规则,并且在局部窗口之内,这种模糊推理规则保持不变.在基于模糊推理的图像放大算法中,根据基于模糊推理的图像模型预测内插像素点的数值,并且这种模糊推理规则可以根据局部像素点的训练得到.实验结果表明,所提出的基于模糊推理的图像放大方法能够有效地提高图像的主客观质量.     

基于模糊免疫PID的多媒体流自调整算法研究

提出了一种基于模糊免疫PID的多媒体流自调整算法(MFIPID),实现对多媒体流的在线自调节和多媒体流资源有效管理。分析多媒体流自适应节模型,实现IP网络可用带宽的预估计,采用全论域范围内带有自调整因子的模糊规则自调整方法进行免疫抑制量的模糊非线性逼近,并与免疫反馈机理相结合,多媒体流传输过程中自适应整定PID参数以改进传统PID算法。仿真验证了MFIPID算法的有效性。     

ATM基于神经模糊算法的呼叫接入控制

本文研究 ＡＴＭ通信网络基于在线测量的呼叫允许接入控制问题。文章提出利用神经－模糊算法在线协调模糊规则中的参数，并在此基础上，实现了一种新的ＣＡＣ机制。在确保服务质量的情况下，提高网络资源利用率，通过实例仿真表明了应用此种算法的可行性。     

改进的基于规则的逆向模糊推理算法

给出了一种改进的基于规则的逆向模糊推理算法.该算法基于产生式知识表示的模糊Petri网(FPN)模型,适用于一类基于规则的系统.利用该算法可以有效地对该类系统的FPN模型进行相应的处理.对于任意指定的库所,通过该算法可以确定其模糊托肯值,即对应命题的模糊真值.通过对具体的算例进行分析并与已有的算法进行比较后发现,该算法不仅可以得到同样精确的结果,而且该算法使FPN的推理过程更类似于人脑的逻辑推导过程,比较缜密.     

A Type-2 Fuzzy Switching Control System for Biped Robots

In this paper, a type-2 fuzzy switching control system is proposed for a biped robot, which includes switched nonlinear system modeling, type-2 fuzzy control system design, and a type-2 fuzzy modeling algorithm. A new switched system model is proposed to represent the continuous-time dynamic and discrete-event dynamic of a walking biped as a whole, which is helpful to analyze the closed-loop stability of the biped locomotion. A type-2 fuzzy switching control system is proposed for the switched system model to guarantee the gait stability and to achieve a robust control performance with a simplified control scheme. Finally, we propose a new fuzzy c-mean variance algorithm for the type-2 fuzzy system modeling to capture the variance of each clustering means, which can translate random uncertainties of original data into rule uncertainties. Simulation results are reported to show the performance of the proposed control system model and algorithms.     

Neurofuzzy-Based Approach to Mobile Robot Navigation in Unknown Environments

In this paper, a neurofuzzy-based approach is proposed, which coordinates the sensor information and robot motion together. A fuzzy logic system is designed with two basic behaviors, target seeking and obstacle avoidance. A learning algorithm based on neural network techniques is developed to tune the parameters of membership functions, which smooths the trajectory generated by the fuzzy logic system. Another learning algorithm is developed to suppress redundant rules in the designed rule base. A state memory strategy is proposed for resolving the "dead cycle" problem. Under the control of the proposed model, a mobile robot can adequately sense the environment around, autonomously avoid static and moving obstacles, and generate reasonable trajectories toward the target in various situations without suffering from the "dead cycle" problems. The effectiveness and efficiency of the proposed approach are demonstrated by simulation studies.     

A CVD epitaxial deposition in a vertical barrel reactor: processmodeling using cluster-based fuzzy logic models

A chemical vapor deposition (CVD) epitaxial deposition process modeling using fuzzy logic models (FLM's) has been proposed. The process modeling algorithm consists of a cluster estimation method and backpropagation algorithm to construct a number of modeling structures from the training data. A decision rule based on the multiple correlation factor is used to obtain the optimum structure of the fuzzy model using the testing data. Upon the optimum structure being reached, the gradient-descent method is used to refer the parameters of the final fuzzy model using both training and testing data. The algorithm has been applied to a nonlinear function and a vertical chemical vapor deposition process. The results demonstrate the efficiency and effectiveness of the proposed fuzzy logic model in comparison with existing fuzzy logic models and artificial neural network models     

Nonlinear system modeling by competitive learning and adaptivefuzzy inference system

Modeling nonlinear systems by neural networks and fuzzy systems encounters problems such as the conflict between overfitting and good generalization and low reliability, which requires a great number of fuzzy rules or neural nodes and uses very complicated learning algorithms. A new adaptive fuzzy inference system, combined with a learning algorithm, is proposed to cope with these problems. First, the algorithm partitions the input space into some local regions by competitive learning, then it determines the decision boundaries for local input regions, and finally, based on the decision boundaries, it learns the fuzzy rule for each local region by recursive least squares (RLS). In the learning algorithm, the key role of the decision boundaries is highly emphasized. To demonstrate the validity of the proposed learning approach and the new adaptive fuzzy inference system, four examples are studied by the proposed method and compared with the previous results     

Neurofuzzy modeling of chemical vapor deposition processes

The modeling of semiconductor manufacturing processes has been the subject of intensive research efforts for years. Physical-based (first-principle) models have been shown to be difficult to develop for processes such as plasma etching and plasma deposition, which exhibit highly nonlinear and complex multidimensional relationships between input and output process variables. As a result, many researchers have turned to empirical techniques to model many semiconductor processes. This paper presents a neurofuzzy approach as a general tool for modeling chemical vapor deposition (CVD) processes. A five-layer feedforward neural network is proposed to model the input-output relationships of a plasma-enhanced CVD deposition of a SiN film. The proposed five-layer network is constructed from a set of input-output training data using unsupervised and supervised neural learning techniques. Product space data clustering is used to perform the partitioning of the input and output spaces. Fuzzy logic rules that describe the input-output relationships are then determined using competitive learning algorithms. Finally, the fuzzy membership functions of the input and output variables are optimally adjusted using the backpropagation learning algorithm. A salient feature of the proposed neurofuzzy network is that after the training process, the internal units are transparent to the user, and the input-output relationship of the CVD process can be described linguistically in terms of IF-THEN fuzzy rules. Computer simulations are conducted to verify the validity and the performance of the proposed neurofuzzy network for modeling CVD processes     

Generating optimal adaptive fuzzy-neural models of dynamicalsystems with applications to control

The paper describes an approach to generating optimal adaptive fuzzy neural models from I/O data. This approach combines structure and parameter identification of Takagi-Sugeno-Kang (TSK) fuzzy models. We propose to achieve structure determination via a combination of modified mountain clustering (MMC) algorithm, recursive least squares estimation (RLSE), and group method of data handling (GMDH). Parameter adjustment is achieved by training the initial TSK model using the algorithm of an adaptive network based fuzzy inference system (ANFIS), which employs backpropagation (BP) and RLSE. Further, a procedure for generating locally optimal model structures is suggested. The structure optimization procedure is composed of two phases: 1) locally optimal rule premise variables subsets (LOPVS) are identified using MMC, GMDH, and a search tree (ST); and 2) locally optimal numbers of model rules (LONOR) are determined using MMC/RLSE along with parallel simulation mean square error (PSMSE) as a performance index. The effectiveness of the proposed approach is verified by a variety of simulation examples. The examples include modeling of a nonlinear dynamical process from I/O data and modeling nonlinear components of dynamical plants, followed by tracking control based on a model reference adaptive scheme (MRAC). Simulation results show that this approach is fast and accurate and leads to several optimal models     

Neural-network-based fuzzy model and its application to transientstability prediction in power systems

We present a general approach to deriving a new type of neural network-based fuzzy model for a complex system from numerical and/or linguistic information. To efficiently identify the structure and the parameters of the new fuzzy model, we first partition the output space instead of the input space. As a result, the input space itself induces corresponding partitions within each of which inputs would have similar outputs. Then we use a set of hyperrectangles to fit the partitions of the input space. Consequently, the premise of an implication in the new type of fuzzy rule is represented by a hyperrectangle and the consequence is represented by a fuzzy singleton. A novel two-layer fuzzy hyperrectangular composite neural network (FHRCNN) can be shown to be computationally equivalent to such a special fuzzy model. The process of presenting input data to each hidden node in a FHRCNN is equivalent to firing a fuzzy rule. An efficient learning algorithm was developed to adjust the weights of an FHRCNN. Finally, we apply FHRCNNs to provide real-time transient stability prediction for use with high-speed control in power systems. From simulation tests on the IEEE 39-bus system, it reveals that the proposed novel FHRCNN can yield a much better performance than that of conventional multilayer perceptrons (MLP's) in terms of computational burden and classification rate     

Design of a Fuzzy Agent that infers the Student’s Self-Efficacy in the context of Learning Envionment over the Web

This article describes an approach to distance learning based on beliefs of Self-Efficacy monitored by a fuzzy agent. More precisely, we propose an adaptable agent that takes part in InteliWeb, a learning environment over the Web that manages the student model taking into account the inferences associated to the beliefs of Self-Efficacy. Our research aims at a student model that can comprehend the necessary dynamics to support the individuality of the student by modeling the beliefs of Self-Efficacy through a fuzzy inference machine.     

Learning of Hybrid Fuzzy Controller for the Optical Data Storage Device

A hybrid track-seeking fuzzy controller for an optical disk drive (ODD) is proposed in this paper. The proposed hybrid fuzzy controller (HFC) smoothes the voltage applied to the sled motor and improves the track-seeking efficiency. The HFC consists of two subsystems including an intelligent time switch and a driving force controller. Both subsystems are designed based on fuzzy logic inferences. The main functions of the proposed HFC are to drive the optical head unit (OHU) to the target track neighborhood as fast as possible and smoothly park the OHU in the least time in the target track neighborhood. An automatic learning approach based on genetic algorithms (GAs) is proposed for learning the fuzzy rules for both the intelligent time switch and driving force controller. Modulated orthogonal membership functions are utilized in both fuzzy controllers to improve the GA learning efficiency. The number of parameters needed to parameterize the fuzzy rule base is greatly reduced with the modulated orthogonal membership functions. Compared to the conventional track-seeking controller currently utilized in most ODDs that employ a speed profile as the reference signal for the track-seeking feedback control system, the proposed HFC outperforms the conventional track-seeking control schemes. Experiments are performed to justify the performance comparison.     

一种基于模糊神经网络的非线性系统模型辨识方法

该文提出一种非线性系统的模型辨识方法。通过结构的辨识(学习)和参数的辨识(学习),构造了一个模糊神经网络,经调整网络的权值,获得一个精确的模糊模型。对两个非线性系统辨识的仿真结果验证了该方法的有效性。