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Simulating Continuous Fuzzy Systems - James J. Buckley下载
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2019-06-26 10:30:13
Simulating Continuous Fuzzy Systems - James J. Buckley
Fuzzy System 经典
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Simulating Continuous Fuzzy Systems - James J. Buckley下载
Simulating Continuous Fuzzy Systems - James J. Buckley Fuzzy System 经典 相关下载链接://download.csdn.net/download/snakeeking/2673397?utm_source=bbsseo
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Simula
ting
Continu
ous
Fuzz
y Systems -
James
J.
Buckley
Simula
ting
Continu
ous
Fuzz
y Systems -
James
J.
Buckley
Fuzz
y System 经典
MATLAB code
simula
ting
different MIMO-OFDM schemes.zip
MATLAB code
simula
ting
different MIMO-OFDM schemes.zip
Fuzz
y and Neuro-
Fuzz
y Systems in Medicine
Preface About the Editors Part 1—Fundamentals and Neuro-
Fuzz
y Signal Processing Chapter 1—
Fuzz
y Logic and Neuro-
Fuzz
y Systems in Medicine and Bio-Medical Engineering: A Historical Perspective 1. The First Period: The Infancy 2. Further Developments and Background 3. Neuro-
Fuzz
y Systems and their Applications in Medicine and Biology 4. Genetic Algorithms,
Fuzz
y Logic, and Neuro-
Fuzz
y Systems 5. Bibliographies 6. Conclusions and Predictions Chapter 2—The Brain as a
Fuzz
y Machine: A Modeling Problem 1. The
Fuzz
y Approach in Neurobiology: A Historical Perspective 2. The Generality of Young’s Hypothesis 2.1 Simple Stimuli 2.2 Neural Organization of Cryptic Events: From Tastes to Faces 2.2.1 The General Approach 2.2.2 Solutions Possible: Taste 2.2.3 Solutions Possible: Faces 2.3 Neural Codes 3.
Fuzz
y Models for Taste 3.1 Grades of Membership in
Fuzz
y Sets 3.2 A
Fuzz
y Model 3.2.1 The Model 3.2.2 The Synthesis of the
Fuzz
y Model 3.2.3
Simula
ting
the Dynamics of Taste Neurons 4.
Fuzz
y Model For Brain Activity 4.1 A Neural Network Implemen
ting
a
Fuzz
y Machine? 4.2 An Artificial Neuron Implements a
Fuzz
y Membership Function 4.3 A Layer of Neurons Implements a
Fuzz
ifier 4.4 A “Hidden” Neuron Implements a
Fuzz
y Rule 5. Applications of
Fuzz
y Logic to Neural Systems 5.1 Quantitative Aspects of the
Fuzz
y Neural Sets 5.1.1 Neural Mass 5.1.2 Sensitivity to Fine Gradations in Input 5.1.3 Intelligence 5.2 De
fuzz
ification and Responses 5.3 Memory: Input and Retrieval 6. Conclusions Appendix 1. Abbreviations Appendix 2. Terminology References Chapter 3—Brain State Identification and Forecas
ting
of Acute Pathology Using Unsupervised
Fuzz
y Clustering of EEG Temporal Patterns 1. Introduction 2. Background 2.1 The Electroencephalogram (EEG) Signal [1], [2] 2.2 Brain States and the EEG 2.3 Stimulus-Evoked EEG Patterns 2.4 Underlying Processes 2.5
Fuzz
y Systems and the EEG 3. Tools 3.1 Data Acquisition 3.1.1 Spontane
ous
Ongoing Signal 3.1.2 Evoked Responses 3.2 Feature Extraction 3.2.1 Spectrum Estimation 3.2.2 Time-Frequency Analysis 3.2.2.1 Multiscale Decomposition By The Fast Wavelet Transform 3.2.2.2 Multichannel Model-Based Decomposition by Matching Pursuit 3.3 The Unsupervised Optimal
Fuzz
y Clustering (UOFC) Algorithm. 3.4 The Weighted
Fuzz
y K-Mean (WFKM) Algorithm 3.5 The Clustering Validity Criteria 4. Examples of Uses 4.1 Sleep-Stage Scoring 4.2 Forecas
ting
Epilepsy 4.3 Classifying Evoked and Event-Related Potentials by Waveform 5. Concluding Remarks and Future Applications 5.1 Dynamic Version of State Identification by UOFC 5.2 Data Fusion Appendiex 1: The Fast Wavelet Transform Appendix 2: Multichannel Model-Based Decomposition by Matching Pursuit Appendix 3: Feature Extraction and Reduction by Principal Component Analysis List of Acronyms References Chapter 4—Contouring Blood Pool Myocardial Gated SPECT Images with a Sequence of Three Techniques Based on Wavelets, Neural Networks, and
Fuzz
y Logic 1. Introduction 2. Anatomy of the G-SPECT Images 3. Strategy of the Proposed Method 3.1. Overview of the Method 3.2. Wavelets-Based Image Pre-Processing 3.3. Neural Network Based Image Segmentation 3.4.
Fuzz
y Logic-Based Recognition of the Regions of Interest (Ventricles) 3.4.1. Definition of the Required
Fuzz
y Sentences 3.4.2. Combining Neuronal Approaches and
Fuzz
y Logic-Based Inference Systems 3.5. Training the Recognition System Using a Neuro-
Fuzz
y Technique 3.5.1. Automated Generation of Rules and Membership Functions (ALGORAM) 3.5.2. Adjustment of Membership Functions Using a Descent Method (FUNNY) 3.5.3. Combining the Automated Generation of Rules and Membership Functions and the Adjustment of their Parameters in a Parallel Implementation (FUNNY-ALGORAM) 4. In Vitro Experiments and Application to Medical Cases 4.1. Experiments with Phantoms 4.2. Clinical Test Cases 4.3. Implementation Issues 5. Conclusions References Chapter 5—Unsupervised Brain Tumor Segmentation Using Knowledge-Based
Fuzz
y Techniques 1. Introduction 2. Domain Background 2.1 Slices of Interest for the Study 2.2 Basic MR Contrast Principles 2.3 Knowledge-Based Systems 2.4 System Overview 3. Classification Stages 3.1 Stage Zero: Pathology Detection 3.2 Stage One: Building the Intra-Cranial Mask 3.3 Stage Two: Multi-spectral Histogram Thresholding 3.4 Stage Three: “Density Screening” in Feature Space 3.5 Stage Four: Region Analysis and Labeling 3.5.1 Removing Meningial Regions 3.5.2 Removing Non-Tumor Regions 3.6 Stage Five: Final T1 Threshold 4. Results 4.1 Knowledge-Based vs. Supervised Methods 4.2 Evaluation Over Repeat Scans 5. Discussion References Abbreviations Part 2—Neuro-
Fuzz
y Knowledge Processing Chapter 6—An Identification of Handling Uncertainties Within Medical Screening: A Case Study Within Screening for Breast Cancer 1. Introduction 2. Screening 2.1 Notations 2.2 The Screening Program 2.3 The Methods 3. The Select Function 3.1 The Decision Step 3.2 Disease-Specific Knowledge 3.3 The Refinement Step 4. A Breast Cancer Case Study 4.1 Minimizing A0 as Much as Possible in One Step 4.2 Finding the Screening Method 4.3 Defining Disease-Specific Knowledge 4.4 Performing the Refinement 4.5 The Integrated System 5. Conclusions and Further Work References Chapter 7—A
Fuzz
y System For Dental Developmental Age Evaluation 1. Introduction 2. Technical Consideration 2.1 Basic Conception of the Teeth Evaluation System 2.2 Rule Evaluation Module 3. System Optimization by Using Clinical Data 3.1 Material and Method 3.2 Dimensionality Analysis by Principal Component Analysis 3.3 System Optimization by Using Genetic Algorithm 3.4 System Evaluation and Results 4. Discussion and Conclusions Chapter 8—
Fuzz
y Expert System For Myocardial Ischemia Diagnosis 1. Introduction 2.
Fuzz
y Expert Systems 3. Difus - Hierarchical Diagnosis
Fuzz
y System 3.1 Characteristics 3.2 Knowledge Organization 3.3 Structure 3.4 Operation 4. Multimethod Myocardial Ischemia Diagnosis 5. Multimethod Myocardial Ischemia Diagnosis System 5.1 The Implementation of
Fuzz
y Score-Based Tests 5.1.1 Medical Patterns 5.1.2 Sequential Processing 5.1.3 Compact Representation of
Fuzz
y Score-Based Tests 5.2 MMIDS Structure and Operation 5.2.1 MMIDS Secondary Group 5.2.2 MMIDS Primary Groups 5.3 Experimental Results 6. Conclusions References Chapter 9—Design and Tuning of
Fuzz
y Rule-Based Systems for Medical Diagnosis 1. Introduction 2. Problem Statement and General Methodology 3. Design and Rough Tuning of
Fuzz
y Rules 3.1. Matrix of Knowledge 3.2.
Fuzz
y Model with Discrete Output 3.3.
Fuzz
y Model with
Continu
ous
Output 3.4. Rough Tuning of
Fuzz
y Rules 3.4.1. Rough Tuning of Membership Functions 3.4.2. Rough Tuning of Rules Weights 4. Fine Tuning of the
Fuzz
y Rules with
Continu
ous
Output 4.1. Tuning as a Problem of Optimization 4.2. Quality Evaluation of
Fuzz
y Inference 4.3. Computer
Simula
tion 4.3.1. Experiment 1 4.3.2. Experiment 2 5. Fine Tuning of the
Fuzz
y Rules with Discrete Output 5.1. Tuning as a Problem of Optimization 5.2. Quality Evaluation of
Fuzz
y Inference 5.3. Computer
Simula
tion 6. Application to Differential Diagnosis of Ischemia Heart Disease 6.1. Diagnosis Types and Parameters of Patient’s State 6.2.
Fuzz
y Rules 6.3.
Fuzz
y Logic Equation 6.4. Rough Membership Functions 6.5. Algorithm of Decision Making 6.6. Fine Tuning of The
Fuzz
y Rules in Medical Applications 7. Conclusions References Appendix 1—Comparison of real and inferred decisions for 65 patients Appendix 2—
FUZZ
Y EXPERT Shell and its Application Chapter 10—Integration of Medical Knowledge in an Expert System for Use in Intensive Care Medicine 1. Introduction 2. Software Design Principles 3. Medical Knowledge in Intensive Care Medicine 3.1. Structure of the Knowledge 3.2. Meaning of Colloquial Rules 3.3. Rule Processing and Result Calculation 3.4. Combining Different Rules 4. Transformation of Knowledge into FLORIDA Commands 4.1. Introduction 4.2. Comments 4.3. Modules 4.4. Linguistic Variables 4.5. The FLORIDA Calculator 4.6. Rules: The Knowledge Itself 4.7. Changing the Normal Value 5. Invocation of FLORIDA 6. Explaining More of FLORIDA’s Functionality — The Knowledge Base Inflammation 6.1. Structuring the Knowledge 6.2. Rules for Fever 6.3. Rules for Leukocytosis/Leukopenia 6.4. Rules for Tachycardia/Tachypnoe 6.5. Rules for Synthesis of Acute Phase Proteins 6.6. Rules for Consumption of Coagulation Components 6.7. Improvement of Explanation 7. Differentiation of Dysfunctions 8. Visualization of the Result 9. Discussion and Conclusions References Part 3—Neuro-
Fuzz
y Control and Hardware Chapter 11—Hemodynamic Management with Multiple Drugs using
Fuzz
y Logic 1. Introduction 1.1 Progress in Decision Making 1.2 Progress in Control 2. System Development 2.1 Decision-Making:
Fuzz
y Decision-Making Module (FDMM) 2.1.1 Purpose 2.1.2 Operation 2.2 Drug-Titration Control:
Fuzz
y Hemodynamic Control Module (FHCM) 2.2.1 Purpose 2.2.2 Operation 2.3 Supervisory Commands: Therapeutic Assessment Module (TAM) 2.4 System Evaluations 2.4.1 Example One 2.4.2 Example Two 3. Future Prospects 3.1 Design Possibilities 3.2 “Curse of Dimensions” 3.3 Machine Intelligence Additional Resources Appendix: Terminology References Chapter 12—Neuro-
Fuzz
y Hardware in Medical Applications 12 A.—System Requirements for
Fuzz
y and Neuro-
Fuzz
y Hardware in Medical Equipment 1. Introduction 2. Specific Requirements of Medical Applications 2.1 General System and Technological Requirements 2.2 Reliability Requirements 2.3 Precision and Sensitivity to Parameters 3. Analysis of Several Applications 3.1 Life-Support Applications 3.1.1 Artificial Heart Control 3.1.2 Assisted Ventilation 3.2 Anesthesia Related Equipment 3.3
Fuzz
y and Neuro-
Fuzz
y-Based Equipment for Prosthetics 3.4 General Purpose Devices 3.5 Other Applications 4. General System Design Issues 4.1 Nonlinearity Implementation -
Simula
tion Power 4.2 Dynamic Errors 5. Hardware Implementation Issues 5.1 Implementation Choice: Analog vs. Digital
Fuzz
y Processors 5.2 Hardware Minimization 5.3 Parallelism vs. Number of Rule Blocks 5.4 A Minimal System Design 6. Choosing the Right Design 7. Conclusions References Chapter 12 B—Neural Networks and
Fuzz
y-Based Integrated Circuit and System Solutions Applied to the Biomedical Field 1. Introduction 2. Required Properties for Embedded Medical Systems 2.1 Embedding medical systems 2.2 Autonomy 2.3 Reliability - safety 2.4 Precision of computation 2.5 Application-specific requirements 3. Architectures Applied to Neuro-
Fuzz
y IC Design 3.1 Artificial Neural Network Integrated Realization 3.2
Fuzz
y-Based Integrated Realization 3.3 Hybrid Integrated Realization 3.4 An example of neuro-
fuzz
y realization 4. Concluding Remarks References Index of Terms
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ting
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wireless communication systems C++代码
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