A general framework for image fusion based on multi-scale transform and sparse representation代码
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Image Fusion With Convolutional Sparse Representation一文
Image Fusion With Convolutional Sparse Representation一文
Multi-sensor image fusion using discrete wavelet frame transform
An algorithm is presented for multi-sensor image fusion using discrete wavelet frame transform (DWFT). The source images to be fused are firstly decomposed by DWFT. The fusion process is the combining of the source coefficients. Before the image fusion process, image segmentation is performed on each source image in order to obtain the region representation of each source image. For each source image, the salience of each region in its region representation is calculated. By overlapping all thes
Separable vocabulary and feature fusion for image retrieval based on sparse representation
Separable vocabulary and feature fusion for image retrieval based on sparse representation
Evaluation-Metrics-for-Image-Fusion-master_evaluation_imagefusio
image fusion evaluation
A novel image fusion rule based on Structure Similarity indices
结构相似性的图像融合,融合效果很好，可以作为一篇经典文章进行学习
Decision fusion of sparse representation and support vector machine for SAR image target recognition
We propose a decision fusion method of Sparse Representation (SR) and Support Vector Machine (SVM) for Synthetic Aperture Radar (SAR) image target recognition in this paper. First, a fast SR classifier (FSR-C) with Matching Pursuit (MP) solution is proposed. In the FSR-C, the dictionary is composed of training images. Just one nonzero element in SR coefficient of the testing image is found out based on MP, and the testing image is classified through the location of the nonzero element. To furthe
Spectral-Spatial Hyperspectral Image Classification via Multiscale Adaptive Sparse Representation
Sparse representation has been demonstrated to be a powerful tool in classification of hyperspectral images (HSIs). The spatial context of an HSI can be exploited by first defining a local region for each test pixel and then jointly representing pixels within each region by a set of common training atoms (samples). However, the selection of the optimal region scale (size) for different HSIs with different types of structures is a nontrivial task. In this paper, considering that regions of differ
CSR-Fusion-master_csr_CSRFusion_imagefusion_
论文《Image fusion with convolutional sparse representation》源代码
image fusion
图像融合 通过最简单的实例 说明图像融合这一复杂的原理，可以通过修改融合规则来获取不同的融合效果
LytroDataset
==================================================================================== Lytro Multi-focus Dataset: This dataset contains 20 pairs of color multi-focus images of size 520?20 pixels and four series of multi-focus images with three sources. Please cite the following paper if you use this dataset: M. Nejati, S. Samavi, S. Shirani, "Multi-focus Image Fusion Using Dictionary-Based Sparse Representation", Information Fusion, vol. 25, Sept. 2015, pp. 72-84. doi:10.1016/j.inffus.2014.10.004 ==================================================================================== https://mansournejati.ece.iut.ac.ir/content/lytro-multi-focus-dataset 其中 all-in-focus图像采用Multi-scale weighted gradient-based fusion for multi-focus images合成的
Fuzzy and Neuro-Fuzzy Systems in Medicine
Preface About the Editors Part 1—Fundamentals and Neuro-Fuzzy Signal Processing Chapter 1—Fuzzy Logic and Neuro-Fuzzy Systems in Medicine and Bio-Medical Engineering: A Historical Perspective 1. The First Period: The Infancy 2. Further Developments and Background 3. Neuro-Fuzzy Systems and their Applications in Medicine and Biology 4. Genetic Algorithms, Fuzzy Logic, and Neuro-Fuzzy Systems 5. Bibliographies 6. Conclusions and Predictions Chapter 2—The Brain as a Fuzzy Machine: A Modeling Problem 1. The Fuzzy 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. Fuzzy Models for Taste 3.1 Grades of Membership in Fuzzy Sets 3.2 A Fuzzy Model 3.2.1 The Model 3.2.2 The Synthesis of the Fuzzy Model 3.2.3 Simulating the Dynamics of Taste Neurons 4. Fuzzy Model For Brain Activity 4.1 A Neural Network Implementing a Fuzzy Machine? 4.2 An Artificial Neuron Implements a Fuzzy Membership Function 4.3 A Layer of Neurons Implements a Fuzzifier 4.4 A “Hidden” Neuron Implements a Fuzzy Rule 5. Applications of Fuzzy Logic to Neural Systems 5.1 Quantitative Aspects of the Fuzzy Neural Sets 5.1.1 Neural Mass 5.1.2 Sensitivity to Fine Gradations in Input 5.1.3 Intelligence 5.2 Defuzzification and Responses 5.3 Memory: Input and Retrieval 6. Conclusions Appendix 1. Abbreviations Appendix 2. Terminology References Chapter 3—Brain State Identification and Forecasting of Acute Pathology Using Unsupervised Fuzzy 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 Fuzzy Systems and the EEG 3. Tools 3.1 Data Acquisition 3.1.1 Spontaneous 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 Fuzzy Clustering (UOFC) Algorithm. 3.4 The Weighted Fuzzy K-Mean (WFKM) Algorithm 3.5 The Clustering Validity Criteria 4. Examples of Uses 4.1 Sleep-Stage Scoring 4.2 Forecasting 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 Fuzzy 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. Fuzzy Logic-Based Recognition of the Regions of Interest (Ventricles) 3.4.1. Definition of the Required Fuzzy Sentences 3.4.2. Combining Neuronal Approaches and Fuzzy Logic-Based Inference Systems 3.5. Training the Recognition System Using a Neuro-Fuzzy 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 Fuzzy 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-Fuzzy 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 Fuzzy 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—Fuzzy Expert System For Myocardial Ischemia Diagnosis 1. Introduction 2. Fuzzy Expert Systems 3. Difus - Hierarchical Diagnosis Fuzzy 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 Fuzzy Score-Based Tests 5.1.1 Medical Patterns 5.1.2 Sequential Processing 5.1.3 Compact Representation of Fuzzy 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 Fuzzy Rule-Based Systems for Medical Diagnosis 1. Introduction 2. Problem Statement and General Methodology 3. Design and Rough Tuning of Fuzzy Rules 3.1. Matrix of Knowledge 3.2. Fuzzy Model with Discrete Output 3.3. Fuzzy Model with Continuous Output 3.4. Rough Tuning of Fuzzy Rules 3.4.1. Rough Tuning of Membership Functions 3.4.2. Rough Tuning of Rules Weights 4. Fine Tuning of the Fuzzy Rules with Continuous Output 4.1. Tuning as a Problem of Optimization 4.2. Quality Evaluation of Fuzzy Inference 4.3. Computer Simulation 4.3.1. Experiment 1 4.3.2. Experiment 2 5. Fine Tuning of the Fuzzy Rules with Discrete Output 5.1. Tuning as a Problem of Optimization 5.2. Quality Evaluation of Fuzzy Inference 5.3. Computer Simulation 6. Application to Differential Diagnosis of Ischemia Heart Disease 6.1. Diagnosis Types and Parameters of Patient’s State 6.2. Fuzzy Rules 6.3. Fuzzy Logic Equation 6.4. Rough Membership Functions 6.5. Algorithm of Decision Making 6.6. Fine Tuning of The Fuzzy Rules in Medical Applications 7. Conclusions References Appendix 1—Comparison of real and inferred decisions for 65 patients Appendix 2—FUZZY 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-Fuzzy Control and Hardware Chapter 11—Hemodynamic Management with Multiple Drugs using Fuzzy Logic 1. Introduction 1.1 Progress in Decision Making 1.2 Progress in Control 2. System Development 2.1 Decision-Making: Fuzzy Decision-Making Module (FDMM) 2.1.1 Purpose 2.1.2 Operation 2.2 Drug-Titration Control: Fuzzy 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-Fuzzy Hardware in Medical Applications 12 A.—System Requirements for Fuzzy and Neuro-Fuzzy 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 Fuzzy and Neuro-Fuzzy-Based Equipment for Prosthetics 3.4 General Purpose Devices 3.5 Other Applications 4. General System Design Issues 4.1 Nonlinearity Implementation - Simulation Power 4.2 Dynamic Errors 5. Hardware Implementation Issues 5.1 Implementation Choice: Analog vs. Digital Fuzzy 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 Fuzzy-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-Fuzzy IC Design 3.1 Artificial Neural Network Integrated Realization 3.2 Fuzzy-Based Integrated Realization 3.3 Hybrid Integrated Realization 3.4 An example of neuro-fuzzy realization 4. Concluding Remarks References Index of Terms
chengxu.zip_分块融合_图像分块融合_图像稀疏表示_稀疏图像融合_稀疏表示融合
是有关图像处理，基于稀疏表示图像融合，用分块过完备表示
基于稠密SIFT描述符和导引滤波器的无重影多曝光图像融合技术仿真.zip
该文件包含了本论文的MATLAB代码，即无重影多曝光图像融合技术，采用了密集的SIFT描述符和导引滤波器。 The file contains the MATLAB code of our paper Ghost-free multi exposure image fusion technique using dense SIFT descriptor and guided filter. 参考文献： Hayat, Naila, and Muhammad Imran. “Ghost-Free Multi Exposure Image Fusion Technique Using Dense {SIFT} Descriptor and Guided Filter.” Journal of Visual Communication and Image Representation, vol. 62, Elsevier {BV}, July 2019, pp. 295–308, doi:10.1016/j.jvcir.2019.06.002.
contoutlet经典的英文论文
contourlet轮廓波变换为近几年流行的wavelet小波变换衍生而来的，具有很强大的发展前景，这几篇文章就详细介绍了这种新技术，又称为第三代小波技术。
Computational Intelligence:An Introduction
Chapter 1—Preliminaries 1.1. Computational Intelligence: its inception and research agenda 1.2. Organization and readership 1.3. References Chapter 2—Neural Networks and Neurocomputing 2.1. Introduction 2.2. Generic models of computational neurons 2.3. Architectures of neural networks - a basic taxonomy 2.3.1. Radial Basis function neural networks 2.4. Learning in neural networks 2.4.1. Neural networks as universal approximators 2.4.2. Generic modes of learning in neural networks 2.4.3. Performance indexes in training of neural networks 2.5. Selected classes of learning methods 2.5.1. Gradient-based optimization of multivariable functions 2.5.2. Perceptron learning rule 2.5.3. Delta learning rule 2.5.4. Backpropagation learning 2.5.5. Hebbian learning 2.5.6. Competitive learning 2.5.7. Self-organizing maps 2.5.8. Learning in presence directly and indirectly labeled patterns 2.6. Generalization abilities of neural networks 2.7. Enhancements of gradient-based learning in neural networks 2.8. Concluding remarks 2.9. Problems 2.10. References Chapter 3—Fuzzy Sets 3.1. Introduction 3.2. Basic definition 3.3. Types of membership functions 3.4. Characteristics of a fuzzy set 3.5. Membership function determination 3.5.1. Horizontal method of membership estimation 3.5.2. Vertical method of membership estimation 3.5.3. Pairwise comparison method of membership function estimation 3.5.4. Problem specification-based membership determination 3.5.5. Membership estimation as a problem of parametric optimization 3.6. Fuzzy relations 3.7. Set theory operations and their properties 3.8. Triangular norms 3.9. Triangular norms as the models of operations on fuzzy sets 3.10. Information-based characteristics of fuzzy sets 3.10.1. Entropy measure of fuzziness 3.10.2. Energy measure of fuzziness 3.10.3. Specificity of a fuzzy set 3.11. Matching measures 3.11.1. Possibility and necessity measures 3.11.2. Compatibility measure 3.12. Numerical representation of fuzzy sets 3.13. Rough sets 3.14. Rough sets and fuzzy sets 3.15. Shadowed sets 3.16. The frame of cognition 3.16.1. Basic definition 3.16.2. Main properties 3.16.3. Approximation aspects of the frame of cognition 3.16.4. Robustness properties of the frame of cognition 3.17. Probability and fuzzy sets 3.18. Hybrid fuzzy-probabilistic models of uncertainty 3.19. Conclusions 3.20. Problems 3.21. References Chapter 4—Computations with Fuzzy Sets 4.1. Introductory remarks 4.2. The extension principle 4.3. Fuzzy numbers 4.3.1. Basic characteristics 4.3.2. Computing with fuzzy numbers 4.3.3. Accumulation of fuzziness in computing with fuzzy numbers 4.4. Fuzzy rule-based computing 4.4.1. Rules with fuzzy sets 4.4.2. A design of fuzzy rule - based systems 4.4.3. Fuzzy Hebbian learning and associative memory as a realization of rule-based systems 4.5. Fuzzy controller and fuzzy control 4.5.1. Generic concept of fuzzy control 4.5.2. Design principles of the fuzzy controller 4.5.3. Numerical experiments 4.5.4. Fuzzy scheduler 4.6. Rule-based systems with nonmonotonic operations 4.6.1. Nonmonotonic AND and OR operations: a generalization 4.6.2. Estimation problem of the default fuzzy set 4.6.3. Approximate reasoning with defaults 4.7. Conclusions 4.8. Problems 4.9. References Chapter 5—Evolutionary Computing 5.1. Introduction 5.2. Gradient-based and probabilistic optimization as examples of single-point search techniques 5.3. Genetic algorithms - fundamentals and a basic algorithm 5.4. Schemata Theorem - a conceptual backbone of GAs 5.5. From search space to GA search space 5.5.1. Gray coding 5.5.2. Floating point coding 5.6. Exploration and exploitation of the search space 5.7. Experimental studies 5.8. Classes of evolutionary computation 5.8.1. Evolutionary Strategies 5.8.2. Evolutionary Programming 5.8.3. Genetic Programming 5.9. Conclusions 5.10 Problems 5.11. References Chapter 6—Fuzzy Neural Systems 6.1. Introduction 6.2. Neurocomputing in fuzzy set technology 6.3. Fuzzy sets in the technology of neurocomputing 6.4. Fuzzy sets in the preprocessing and enhancements of training data 6.4.1. Nonlinear data normalization 6.4.2. Variable processing resolution - fuzzy receptive fields 6.5. Uncertainty representation in neural networks 6.6. Neural calibration of membership functions 6.6.1. The Optimization Algorithm 6.6.2. Neural network realization of the nonlinear mapping 6.7. Knowledge-based learning schemes 6.7.1. Metalearning and fuzzy sets 6.7.2. Fuzzy clustering in revealing relationships within data 6.7.2.1. Fuzzy perceptron 6.7.2.2. Conditional (context-sensitive) clustering as a preprocessing phase in neural networks 6.8. Linguistic interpretation of neural networks 6.8.1. From neural networks to rule-based systems 6.8.2. Linguistic Interpretation of self-organizing maps 6.9. Hybrid fuzzy neural computing structures 6.9.1. Architectures of hybrid fuzzy neural systems 6.9.2. Temporal aspects of interaction in fuzzy-neural systems 6.10. Conclusions 6.11. Problems 6.12. References Chapter 7—Fuzzy Neural Networks 7.1. Logic-based neurons 7.1.1. Aggregative OR and AND logic neurons 7.1.2. OR/AND neurons 7.1.3. Conceptual and computational augmentations of fuzzy neurons 7.1.3.1. Representing inhibitory information 7.1.3.2. Computational enhancements of the neurons 7.2. Logic neurons and fuzzy neural networks with feedback 7.3. Referential logic-based neurons 7.4. Learning in fuzzy neural networks 7.4.1. Learning policies for parametric learning in fuzzy neural networks 7.4.2. Performance index 7.4.3. Interpretation of fuzzy neural networks 7.5. Case studies 7.5.1. Logic filtering 7.5.2. Minimization of multiple output two-valued combinational systems 7.5.3. FNN as a model of approximate reasoning 7.5.4. Sensor fusion via fuzzy neurons 7.6. Conclusions 7.7. Problems 7.8. References Chapter 8—CI systems 8.1. Introduction 8.2. Fuzzy encoding in evolutionary computing 8.2.1. Direct methods of fuzzy encoding 8.2.2. Weak encoding with fuzzy sets 8.3. Fuzzy crossover operations 8.4. Fuzzy metarules in genetic computing 8.5. Relational structures and their optimization 8.5.1. Image compression as a problem of relation reduction 8.5.2. GA-optimized data mining 8.6. The Satisfiability Problem 8.7. Evolutionary rule-based modeling of analytical relationships 8.8. Genetic optimization of neural networks 8.8.1. Parametric optimization of neural networks 8.8.2. Fuzzy genetic optimization of neural networks 8.9. Genetic optimization of rule-based systems 8.10. Conclusions 8.11. Problems 8.12. References Index
Intelligent Adaptive Control: Industrial Applications
Preface Acknowledgments Chapter 1—Intelligent Control Techniques 1 Introduction 2 Knowledge-Based Systems 3 Neural Networks 3.1 Introduction 3.2 Biological Neuronal Morphology 3.3 Static Neural Networks 3.4 Common Types of Artificial Neural Networks 3.5 Backpropagation Learning Algorithm 4 Fuzzy Logic 4.1 Fuzzy Systems and Rules 4.2 Fuzzy Reasoning and Aggregation 4.3 Fuzzy Control 5 Evolutionary Computing 5.1 GA Searching Algorithm 5.2 GA Selection 5.3 GA Reproduction 6 Summary References Chapter 2—Learning and Adaptation in Complex Dynamic Systems 1 Introduction 2 Systems Identification and Adaptive Control 2.1 Techniques for Systems Identification 2.1.1 Nonparametric methods 2.1.2 Parametric methods 2.2 Adaptive Control 2.2.1 Gain scheduling 2.2.2 Model-referenced adaptive control 2.2.3 Self-tuning regulators 3 Learning Techniques 3.1 Symbolic Learning 3.2 Numerical Learning 4 Artificial Neural Networks 4.1 Multilayer Perceptron 4.2 Kohonen Self-Organizing Network 4.3 Neural Networks as Identification Tools 4.4 Other Applications of ANN 5 Summary References Chapter 3—Applications of Evolutionary Algorithms to Control and Design 1 Introduction 1.1 Some Basics 1.2 Types of Evolutionary Algorithms 1.3 Types of Operations 1.3.1 Representation of Chromosomes 1.3.2 Fitness Value 1.3.3 Selection 1.3.4 Recombination and Mutation 1.4 Applications 2 Applications in Control 2.1 Robot Control 2.1.1 Planning of Joint Configuration of Manipulators 2.1.2 Acquisition of Behavior Rules of Autonomous Mobile Robots 2.2 Communication System Control 3 Applications in Design 3.1 Circuit Design 3.2 Graphics Design 3.2.1 Interactive EA Approach 3.2.2 Fitness Estimation in Interactive Approach 3.3 Music Design 3.3.1 Sound Synthesis 3.3.2 Algorithmic Composition 4 Other Applications 5 Summary References Chapter 4—Neural Control Systems and Applications 1 Introduction 2 Artificial Neural Networks (ANN) 2.1 The Backpropagation Network (BPN) 2.2 Kohonen Networks 2.3 Counterpropagation Networks 2.4 Hebbian Networks 2.5 Radial Basis Function Networks 2.6 Hopfield Networks 3 Neural Modeling and Identification 4 Neurocontroller-Design Methods 4.1 Supervised Control 4.2 Direct Inverse Control 4.3 Neural Adaptive Control 4.4 Backpropagation Through Time 4.5 Reinforcement Learning Control 4.6 Hybrid Control 5 Hardware and Software for ANN 6 Modular Neural-Visual Servo Control System 6.1 Introduction 6.2 Modular Neural-Control System 6.2.1 Control Networks 6.2.2 Decision Networks 6.3 Evaluation System 6.4 Preliminary Results 6.5 Conclusion 7 Summary References Chapter 5—Feature Space Neural Filters and Controllers 1 Introduction 2 Feature Space Filtering Using Neural Networks 2.1 Adaptive Signal Processing for Pattern Recognition 2.2 General Structure of the FSF System 2.3 A FSF Architecture Using Adaptive Linear Combiner Filter and Radial Basis Function Network Feature Extractor 2.3.1 FSF Realization 2.3.2 ALC-RBF FSF System Learning 2.3.3 Image Contour Enhancement and Recognition Optimization Using ALC-RBF FSF System 2.3.4 Discussion 2.4 FSF Architecture Using Multilayer Perceptron Filter and Principal Component Analysis Network Feature Extractor 2.4.1 MLP-PCA FSF System Realization 2.4.2 Learning in MLP-PCA FSF System 2.4.3 Example of MLP-PCA Feature Space System in Signal Filtering 2.4.4 Discussion 2.5 Concluding Remarks 3 Feature Space Identification and Control Schemes Using Neural Networks 3.1 Feature Space Neural Identification Topologies 3.2 Feature Space Neural Control Topologies 3.2.1 Data Versus Feature Space Neural Control Topologies 3.2.2 Feature Space Control Example 3.2.3 Identification Neural Network 3.2.3.1 Feature Extractor 3.2.3.2 Control Network 3.3 Discussion and Concluding Remarks 4 Summary References Chapter 6—Discrete-Time Neural Network Control of Nonlinear Systems 1 Introduction 2 Background 2.1 Neural Networks 2.2 Advantage of NN Over Adaptive Controllers 2.3 Stability of Dynamical Systems 2.4 MIMO Dynamical Systems 2.5 Tracking Problem 3 Neural Network Controller Design 3.1 NN Controller Structure and Error System 3.2 Well-Defined Control Problem 3.3 Proposed Controller 3.4 Weight Updates for Guaranteed Performance 4 Passivity of Dynamical Systems 4.1 Passive Systems 4.2 Passivity of the Closed-loop System and NN 5 Simulation Results 6 Summary References Chapter 7—Robust Adaptive Control of Robots Based on Static Neural Networks 1 Introduction 2 Notation 2.1 Permutation Operator “⊗” 2.2 GL Product Operator “•” 3 Neural Network Approximation 4 Lagrange-Euler Formulation of Robots 5 Dynamic Modeling of Robots Using Neural Networks 6 Controller Design 7 Case Study 7.1 Trajectory Planning 7.2 Simulation Settings 7.3 Non-Adaptive Control 7.4 Adaptive Control 8 Summary References Chapter 8—Error Correction Using Fuzzy Logic in Vehicle Load Measurement 1 Introduction 2 Vehicle Load Indicator 3 Describing Vehicle Loading States 4 Fuzzy Reasoning 5 Simulation Experiment 6 Summary References Chapter 9—Intelligent Control of Air Conditioning Systems 1 Fuzzy Controlled Air Conditioning System for Energy Conservation Applied to the Synthetic Fiber Plant 1.1 Introduction 1.2 Role and Problems of Air Conditioning Equipment in Synthetic Fiber Plant 1.2.1 Role of Air Conditioners in Spinning and Drawing/Twisting Processes 1.2.2 Problems of Air Conditioners in Spinning and Drawing/Twisting Processes 1.3 Fuzzy-Controlled Air Conditioning System for Energy Conservation 1.4 Results 1.5 Future Directions 1.6 Acknowledgment 2 A Learning Type Fuzzy Logic Control for Stabilizing Temperature and Humidity in a Clean Room 2.1 Introduction 2.2 Ordinary Fuzzy Logic Control System 2.3 A Learning Type Fuzzy Logic Control System 2.3.1 Structure of a Hierarchical Fuzzy Model 2.3.2 Succession of the Ordinary Fuzzy Model 2.4 Simulation Experiments 2.5 Practical Results 2.6 Conclusion 3 Occupant Condition Detecting Algorithm for Air Conditioning Systems 3.1 Introduction 3.2 Structure of the Pyroelectric Infrared Rays Detector 3.3 Segmentation of Occupants from Thermal Images 3.3.1 Removing Background Using the Fuzzy C-Means Algorithm 3.3.2 Identifying the Number of Occupants 3.3.3 Region Growing Algorithm 3.4 Method to Locate Occupants 3.4.1 Estimating the Distance Between the Sensor and Occupants 3.4.2 Experimental Results 3.5 Conclusion References Chapter 10—Intelligent Automation Systems at Petroleum Plants in Transient State 1 PID Controller Using Neuro-Fuzzy Hierarchical System in Feed Oil Switching 1.1 Introduction 1.2 Process Description 1.3 Control Problems in Feed Oil Switching 1.4 Neuro-Fuzzy Hierarchical Control System 1.4.1 Prediction Function 1.4.2 Correction Function 1.5 Control Algorithm 1.6 Results 1.7 Conclusion 2 Fuzzy Control System in Pump Start-up 2.1 Introduction 2.2 Outline of Pump Start-up Operation 2.3 Problems to Automate by Conventional Controller 2.3.1 Ramp controller 2.3.2 PID controller 2.4 Fuzzy Controller 2.4.1 Input Variable and Output Variable 2.4.2 Fuzzy Control Rules 2.5 Results 2.6 Conclusion 3 Fuzzy-PID Hybrid Control System in Feed Property Changing 3.1 Introduction 3.2 Process Description 3.3 Control Problems 3.4 Fuzzy-PID Hybrid Control System 3.5 Parameter Tuning 3.6 Results 3.7 Conclusion References Chapter 11—Intelligent Control for Ultrasonic Motor Drive 1 Introduction 2 Ultrasonic Motor Drive 2.1 The Equivalent Model of the USM 2.2 The Driving Circuit for the USM 3 Fuzzy Model-Following Control 4 Neural Network Model-Following Control 4.1 Description of the Neural Network 4.2 On-Line Learning Algorithm 5 Fuzzy Neural Network Model-Following Control 5.1 Description of the Fuzzy Neural Network 5.2 On-Line Learning Algorithm 6 The PC-Based Ultrasonic Motor Drive 7 Experimental Results 7.1 Fuzzy Model-Following Control 7.2 Neural Network Model-Following Control 7.3 Fuzzy Neural Network Model-Following Control 8 Summary References Chapter 12—Intelligent Automation of Herring Roe Grading 1 Introduction 2 Herring Roe Grading Process 2.1 Pre-Extraction Stage 2.2 Main Grading 2.3 Price Negotiation 3 Grading Technology 3.1 Shape Analysis 3.2 Ultrasonic Echo Imaging for Firmness Measurement 3.3 Vision-Based Weight Estimation 3.4 Color Grading 3.5 Fuzzy Decision-Making System 4 Prototype Development 4.1 Conveyor System 4.2 Ejection Mechanism 4.3 Sensory System 4.4 Prototype Control System 5 Prototype Testing 5.1 Laboratory Experiments 5.2 On-Site Production Test 5.3 Performance Evaluation and Possible Improvements 6 Summary Acknowledgment References Chapter 13—Intelligent Techniques for Vehicle Driving Assistance 1 Introduction 2 Multisensor Data Fusion 2.1 Introduction 2.2 Sensors 2.2.1 Static Environment 2.2.2 Dynamic Environment 2.3 Temporal Data Fusion 2.3.1 The Static Environment Perception 2.3.2 The Dynamic Environment Perception 2.3.2.1 Definition of the sensor and global maps 2.3.2.2 The different steps of the filtering operation 2.3.2.3 Reliability definition 2.3.3 The Copilot Mapping 2.4 Conclusion 3 Vehicle Modeling for Supervision of Manoeuvres 3.1 Introduction 3.2 Supervision of Manoeuvres 3.3 Situation Analysis 3.3.1 The Dynamic Model of the Vehicle 3.3.2 Vehicle Following 3.3.3 Highway Access Manoeuvre 3.3.4 A Lane-Changing Manoeuvre 3.4 Manoeuvre Monitor 3.5 Danger Controller 3.6 Requests Generation and Sensors Planning 3.7 The Driver Information Level 3.8 Conclusion 4 On-Board Real-Time Expert System for Control of the Vehicle 4.1 Introduction 4.2 Development of an Expert System for Control 4.2.1 Building the Knowledge Base 4.2.2 Basic Functioning of the Expert System for Control 4.3 Development of the Real-Time Expert System 4.3.1 Integration of the Expert System in the Real-Time Environment 4.3.2 Asynchronous Data Flows 4.3.3 Control Strategies for the IP 4.3.4 Interrupt Handling 4.3.5 Temporal Reasoning and Multiagent KBS 4.4 Conclusion 5 Summary Notation References Chapter 14—Intelligent Techniques in Air Traffic Management 1 Introduction 1.1 Future Air Navigation Systems (FANS) 1.2 Air Traffic Management 1.3 ATM System Issues for FANS 1.4 Applying AI Technology to ATC 1.4.1 Scheduling and Planning 1.4.2 Agent Technology 2 Intelligent ATC Systems 2.1 OASIS 2.2 COMPAS 2.3 CTAS 3 Intelligent Air Traffic Flow Management 3.1 A Model of Air Traffic Flow Management 3.2 Scheduling for ATFM 3.3 Heuristics 4 The Air Traffic Simulation Test 4.1 Interactive Plan Steering Architecture 4.2 AirTFM - the ATFM Test 5 Real-Time Search Algorithm for Air Traffic Flow Management 5.1 Real-Time Search Algorithms 5.1.1 Real-Time Planning and Scheduling Algorithms 5.1.2 Real-Time Monitoring and Control Algorithms 5.2 Time-Dependent Heuristic Search (TDHS) 5.3 Complexity Analysis of TDHS 5.4 Time-Dependent Cost Function 5.5 Experimental Results on TDHS 5.5.1 The Traveling Salesperson Problem 5.5.2 Base Performance of Heuristics 5.5.3 Results of TDHS on TSP 6 Summary References Index Copyright © CRC Press LLC
多模态大语言模型综述来啦！一文带你理清多模态关键技术.pdf
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