文件名称:
Fuzzy Sets Engineering
开发工具:
文件大小: 8mb
下载次数: 0
上传时间: 2008-10-06
详细说明： Foreword Dedication Chapter 1—Fuzzy set-based modelling and simulation environment 1.1 Introduction: a rationale 1.2 System modelling with fuzzy sets 1.2.1 The paradigm 1.2.2 The general architecture of fuzzy models and the methodology of their development 1.3 References Chapter 2—Development of input interfaces 2.1 The frame of cognition 2.1.1 Definition 2.1.2 Properties 2.2 Uncertainty representation in the fram e of cognition 2.2.1 Possibility and necessity measures 2.2.2 Taxonomy of uncertainty: conflict and ignorance 2.2.3 Uncertainty representation — activation planes 2.3 Reconstruction criterion 2.4 The criterion of entropy equalization 2.5 References Chapter 3—Fuzzy neural networks: models and learning 3.1 From neural networks and fuzzy sets to fuzzy neurocomputations 3.2 Logic-based neurons 3.2.1 Aggregative OR and AND logic neurons 3.2.2 OR/AND neurons 3.2.3 Logic neurons and an OWA aggregation operator 3.2.4 Computational enhancements of fuzzy neurons 3.2.5 Logic neurons with feedback 3.2.6 Referential logic-based neurons 3.2.7 Fuzzy threshold neuron 3.3 Classes of fuzzy neural networks 3.3.1 Approximation of logical relationships — development of the logic processor 3.3.2 Referential processor 3.4 Learning 3.4.1 Learning a single neuron 3.4.2 General policies for the parametric learning — reductions and expansions 3.5 Genetic Algorithms in structural learning of fuzzy neural networks 3.5.1 Prerequisites — Genetic Algorithms as a tool for global optimization 3.5.2 Hybridization: Gradient-based and genetic-oriented schemes in learning fuzzy neural networks 3.5.3 The stratified GA learning in fuzzy neural networks 3.6 Selected aspects of knowledge representation in fuzzy neural networks 3.6.1 Representing and processing uncertainty 3.6.2 Induced Boolean and core neural networks 3.7 Conclusions 3.8 References Chapter 4—Fuzzy neurocomputations 4.1 Decomposition problem 4.2 Implementation of the Tchebyschev and Hausdorff distances 4.3 Optimal vector quantization 4.4 Neural models of fuzzy decision-making 4.5 Rule induction 4.6 Fuzzy Computational Memories (FCM) 4.6.1 The architecture and its functional modules 4.6.2 Learning 4.7 Implicitly-supervised fuzzy pattern recognition 4.7.1 Problem formulation 4.7.2 The General Architecture 4.7.3 The Design of the Classifier 4.8 Neural network realization of a pseudomedian filter 4.9 Ranking fuzzy sets defined in R 4.10 Conclusions 4.11 References Chapter 5—Development of output interfaces 5.1 Linguistic to numerical mapping 5.1.1 Reconstruction criterion 5.1.2 Triangular fuzzy sets in 5.2 Transformation of nonnumerical inputs 5.3 Fuzzy set reconstruction 5.4 Linguistic interpretation 5.5 Optimization of fuzzy models 5.5.1 Validation of fuzzy models 5.5.2 Hierarchy of memories in fuzzy models and learning policies 5.6 Concluding comments 5.7 References Chapter 6—Fuzzy controller 6.1 The basic architecture 6.2 Fuzzy Hebbian learning 6.3 Compilation and interpretation of fuzzy controllers 6.4 Input and output interfaces of the fuzzy controller 6.4.1 Realization of the output interface 6.4.2 Robustness of the fuzzy controller 6.4.3 Nonnumerical input information 6.5 Validation of the fuzzy controller 6.5.1 Static validation 6.5.1.1 Completeness and structural fault-tolerance 6.5.1.2 Conflict 6.5.2 Dynamic modifications 6.5.2.1 Modifications of the input interface of the controller 6.5.2.2 Rule modification 6.6 Extensions of the fuzzy controller 6.6.1 Fuzzy relational equations as a development framework of fuzzy controllers 6.6.2 Fuzzy logic controller 6.7 Hybrid control structures 6.7.1 Switching between fuzzy controller and PID controller 6.7.2 Supervisory control 6.8 Concluding comments 6.9 References Chapter 7—Software development tools in designing fuzzy systems 7.1 The general development framework of fuzzy inference schemes 7.2 Classes of software resources 7.3 Hardware versus software implementation 7.3.1 High-speed fuzzy controllers: a genuine need or (in)expensive extravagance? 7.4 Selected software development tools 7.4.1 MANIFOLD EDITOR and MANIFOLD GRAPHICS EDITOR 7.4.2 FUZZY LOGIC DESIGNER ver. 1.0 7.4.3 FuzzyTECH 3.0 Explorer Edition 7.4.4 Linguistic Fuzzy Logic Controller for Education LFLC-edu ver. 1.0 7.4.5 Fuzzy Logic Development Kit (FULDEK) 7.4.6 MATRIXx/SystemBuild 7.4.7 A Fuzzy Logic Knowledge base generator for the MC68HC11 and MCH68HC05 Inference Engines 7.4.8 Fuzz-C, a preprocessor for fuzzy logic, ver 1.00 7.4.9 FuziCalc ver. 1.00 for Microsoft Windows 7.5 Designing fuzzy controllers with the use of simulation packages 7.6 Conclusions Chapter 8—Fuzzy Control 8.1 Defining notions of fuzzy control 8.1.1 Notions of a single step and multistep constraint-free control 8.2 Stability 8.3 Controllability with constraint requirements 8.4 Fuzzy controller as a knowledge-based control paradigm 8.4.1 Trial-and-error design policies 8.4.2 The architecture of the fuzzy controller 8.5 Control in fuzzy models 8.5.1 Problem formulation 8.5.2 The architecture 8.5.3 Defining objectives of fuzzy control 8.6 Control determination 8.6.1 On-line computations 8.6.2 Off-line control 8.7 Conclusions 8.8 References Chapter 9—Fuzzy flip-flops in information processing 9.1 From JK flip-flops to fuzzy flip-flops 9.1.1 Two-valued JK flip-flop 9.1.2 Design 9.1.3 Generalized fuzzy flip-flop 9.1.4 Fuzzy flip-flop 9.2 Fuzzy flip-flop and its neural network realization 9.3 System design through learning 9.4 Boolean and core structures of fuzzy flip-flops 9.5 Information processing with fuzzy flip-flops 9.5.1 Distributed modelling and its flip-flop realization 9.5.2 Realization of fuzzy algorithmic state machines 9.5.3 Memory-enhanced fuzzy controller 9.5.4 Dynamical pattern classifier 9.6 Conclusions 9.7 References Chapter 10—Fuzzy petri nets 10.1 Introduction 10.2 Petri nets and their fuzzy set-based extensions 10.3 Fuzzy Petri nets 10.3.1 Models of transitions and places 10.3.2 Boolean analysis of the net 10.4 The neural network model of the fuzzy Petri net and its enhancements 10.4.1 Inhibition mechanism in fuzzy Petri nets and its representation 10.4.2 Modelling input and output places — more detailed neural models 10.5 Examples 10.6 Representing rules in fuzzy Petri nets 10.7 Fuzzy controller realized as a fuzzy Petri net 10.8 Learning 10.9 Inverse problem in fuzzy Petri net 10.10 Conclusion 10.11 References Appendix A Appendix B Appendix C Index ...展开收缩
(系统自动生成,下载前可以参看下载内容)
下载文件列表
相关说明
- 本站资源为会员上传分享交流与学习,如有侵犯您的权益,请联系我们删除.
- 本站是交换下载平台，提供交流渠道，下载内容来自于网络，除下载问题外，其它问题请自行百度。
- 本站已设置防盗链，请勿用迅雷、QQ旋风等多线程下载软件下载资源，下载后用WinRAR最新版进行解压.
- 如果您发现内容无法下载，请稍后再次尝试；或者到消费记录里找到下载记录反馈给我们.
- 下载后发现下载的内容跟说明不相乎，请到消费记录里找到下载记录反馈给我们，经确认后退回积分.
- 如下载前有疑问，可以通过点击"提供者"的名字，查看对方的联系方式，联系对方咨询.
