Cybernetical intelligence : engineering cybernetics with machine intelligence /

Cybernetical intelligence : engineering cybernetics with machine intelligence / Kelvin K. L. Wong.

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CYBERNETICAL INTELLIGENCE Highly comprehensive, detailed, and up-to-date overview of artificial intelligence and cybernetics, with practical examples and supplementary learning resources Cybernetical Intelligence: Engineering Cybernetics with Machine Intelligence is a comprehensive guide to the fiel...

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Bibliographic Details
Online Access: Full Text (via IEEE)
Main Author: Wong, Kelvin K. L. (Author)
Format: Electronic eBook
Language:English
Published: Hoboken, NJ : Wiley : IEEE Press, [2024]
Subjects:
Machine learning.
Cybernetics.
Neural networks (Computer science)
Table of Contents:
  • Preface
  • About the Author
  • About the Companion Website
  • 1 Artificial Intelligence and Cybernetical Learning
  • 1.1 Artificial Intelligence Initiative
  • 1.2 Intelligent Automation Initiative
  • 1.2.1 Benefits of IAI
  • 1.3 Artificial Intelligence Versus Intelligent Automation
  • 1.3.1 Process Discovery
  • 1.3.2 Optimization
  • 1.3.3 Analytics and Insight
  • 1.4 The Fourth Industrial Revolution and Artificial Intelligence
  • 1.4.1 Artificial Narrow Intelligence
  • 1.4.2 Artificial General Intelligence
  • 1.4.3 Artificial Super Intelligence
  • 1.5 Pattern Analysis and Cognitive Learning
  • 1.5.1 Machine Learning
  • 1.5.1.1 Parametric Algorithms
  • 1.5.1.2 Nonparametric Algorithms
  • 1.5.2 Deep Learning
  • 1.5.2.1 Convolutional Neural Networks in Advancing Artificial Intelligence
  • 1.5.2.2 Future Advancement in Deep Learning
  • 1.5.3 Cybernetical Learning
  • 1.6 Cybernetical Artificial Intelligence
  • 1.6.1 Artificial Intelligence Control Theory
  • 1.6.2 Information Theory
  • 1.6.3 Cybernetic Systems
  • 1.7 Cybernetical Intelligence Definition
  • 1.8 The Future of Cybernetical Intelligence
  • Summary
  • Exercise Questions
  • Further Reading
  • 2 Cybernetical Intelligent Control
  • 2.1 Control Theory and Feedback Control Systems
  • 2.2 Maxwell's Analysis of Governors
  • 2.3 Harold Black
  • 2.4 Nyquist and Bode
  • 2.5 Stafford Beer
  • 2.5.1 Cybernetic Control
  • 2.5.2 Viable Systems Model
  • 2.5.3 Cybernetics Models of Management
  • 2.6 James Lovelock
  • 2.6.1 Cybernetic Approach to Ecosystems
  • 2.6.2 Gaia Hypothesis
  • 2.7 Macy Conference
  • 2.8 McCulloch-Pitts
  • 2.9 John von Neumann
  • 2.9.1 Discussions on Self-Replicating Machines
  • 2.9.2 Discussions on Machine Learning
  • Summary
  • Exercise Questions
  • Further Reading
  • 3 The Basics of Perceptron
  • 3.1 The Analogy of Biological and Artificial Neurons
  • 3.1.1 Biological Neurons and Neurodynamics
  • 3.1.2 The Structure of Neural Network
  • 3.1.3 Encoding and Decoding
  • 3.2 Perception and Multilayer Perceptron
  • 3.2.1 Back Propagation Neural Network
  • 3.2.2 Derivative Equations for Backpropagation
  • 3.3 Activation Function
  • 3.3.1 Sigmoid Activation Function
  • 3.3.2 Hyperbolic Tangent Activation Function
  • 3.3.3 Rectified Linear Unit Activation Function
  • 3.3.4 Linear Activation Function
  • Summary
  • Exercise Questions
  • Further Reading
  • 4 The Structure of Neural Network
  • 4.1 Layers in Neural Network
  • 4.1.1 Input Layer
  • 4.1.2 Hidden Layer
  • 4.1.3 Neurons
  • 4.1.4 Weights and Biases
  • 4.1.5 Forward Propagation
  • 4.1.6 Backpropagation
  • 4.2 Perceptron and Multilayer Perceptron
  • 4.3 Recurrent Neural Network
  • 4.3.1 Long Short-Term Memory
  • 4.4 Markov Neural Networks
  • 4.4.1 State Transition Function
  • 4.4.2 Observation Function
  • 4.4.3 Policy Function
  • 4.4.4 Loss Function
  • 4.5 Generative Adversarial Network
  • Summary
  • Exercise Questions
  • Further Reading
  • 5 Backpropagation Neural Network
  • 5.1 Backpropagation Neural Network
  • 5.1.1 Forward Propagation
  • 5.2 Gradient Descent
  • 5.2.1 Loss Function
  • 5.2.2 Parameters in Gradient Descent
  • 5.2.3 Gradient in Gradient Descent
  • 5.2.4 Learning Rate in Gradient Descent
  • 5.2.5 Update Rule in Gradient Descent
  • 5.3 Stopping Criteria
  • 5.3.1 Convergence and Stopping Criteria
  • 5.3.2 Local Minimum and Global Minimum
  • 5.4 Resampling Methods
  • 5.4.1 Cross-Validation
  • 5.4.2 Bootstrapping
  • 5.4.3 Monte Carlo Cross-Validation
  • 5.5 Optimizers in Neural Network
  • 5.5.1 Stochastic Gradient Descent
  • 5.5.2 Root Mean Square Propagation
  • 5.5.3 Adaptive Moment Estimation
  • 5.5.4 AdaMax
  • 5.5.5 Momentum Optimization
  • Summary
  • Exercise Questions
  • Further Reading
  • 6 Application of Neural Network in Learning and Recognition
  • 6.1 Applying Backpropagation to Shape Recognition
  • 6.2 Softmax Regression
  • 6.3 K-Binary Classifier
  • 6.4 Relational Learning via Neural Network
  • 6.4.1 Graph Neural Network
  • 6.4.2 Graph Convolutional Network
  • 6.5 Cybernetics Using Neural Network
  • 6.6 Structure of Neural Network for Image Processing
  • 6.7 Transformer Networks
  • 6.8 Attention Mechanisms
  • 6.9 Graph Neural Networks
  • 6.10 Transfer Learning
  • 6.11 Generalization of Neural Networks
  • 6.12 Performance Measures
  • 6.12.1 Confusion Matrix
  • 6.12.2 Receiver Operating Characteristic
  • 6.12.3 Area Under the ROC Curve
  • Summary
  • Exercise Questions
  • Further Reading
  • 7 Competitive Learning and Self-Organizing Map
  • 7.1 Principal of Competitive Learning
  • 7.1.1 Step 1: Normalized Input Vector
  • 7.1.2 Step 2: Find the Winning Neuron
  • 7.1.3 Step 3: Adjust the Network Weight Vector and Output Results
  • 7.2 Basic Structure of Self-Organizing Map
  • 7.2.1 Properties Self-Organizing Map
  • 7.3 Self-Organizing Mapping Neural Network Algorithm
  • 7.3.1 Step 1: Initialize Parameter
  • 7.3.2 Step 2: Select Inputs and Determine Winning Nodes
  • 7.3.3 Step 3: Affect Neighboring Neurons
  • 7.3.4 Step 4: Adjust Weights
  • 7.3.5 Step 5: Judging the End Condition
  • 7.4 Growing Self-Organizing Map
  • 7.5 Time Adaptive Self-Organizing Map
  • 7.5.1 TASOM-Based Algorithms for Real Applications
  • 7.6 Oriented and Scalable Map
  • 7.7 Generative Topographic Map
  • Summary
  • Exercise Questions
  • Further Reading
  • 8 Support Vector Machine
  • 8.1 The Definition of Data Clustering
  • 8.2 Support Vector and Margin
  • 8.3 Kernel Function
  • 8.3.1 Linear Kernel
  • 8.3.2 Polynomial Kernel
  • 8.3.3 Radial Basis Function
  • 8.3.4 Laplace Kernel
  • 8.3.5 Sigmoid Kernel
  • 8.4 Linear and Nonlinear Support Vector Machine
  • 8.5 Hard Margin and Soft Margin in Support Vector Machine
  • 8.6 I/O of Support Vector Machine
  • 8.6.1 Training Data
  • 8.6.2 Feature Matrix and Label Vector
  • 8.7 Hyperparameters of Support Vector Machine
  • 8.7.1 The C Hyperparameter
  • 8.7.2 Kernel Coefficient
  • 8.7.3 Class Weights
  • 8.7.4 Convergence Criteria
  • 8.7.5 Regularization
  • 8.8 Application of Support Vector Machine
  • 8.8.1 Classification
  • 8.8.2 Regression
  • 8.8.3 Image Classification
  • 8.8.4 Text Classification
  • Summary
  • Exercise Questions
  • Further Reading
  • 9 Bio-Inspired Cybernetical Intelligence
  • 9.1 Genetic Algorithm
  • 9.2 Ant Colony Optimization
  • 9.3 Bees Algorithm
  • 9.4 Artificial Bee Colony Algorithm
  • 9.5 Cuckoo Search
  • 9.6 Particle Swarm Optimization
  • 9.7 Bacterial Foraging Optimization
  • 9.8 Gray Wolf Optimizer
  • 9.9 Firefly Algorithm
  • Summary
  • Exercise Questions
  • Further Reading
  • 10 Life-Inspired Machine Intelligence and Cybernetics
  • 10.1 Multi-Agent AI Systems
  • 10.1.1 Game Theory
  • 10.1.2 Distributed Multi-Agent Systems
  • 10.1.3 Multi-Agent Reinforcement Learning
  • 10.1.4 Evolutionary Computation and Multi-Agent Systems
  • 10.2 Cellular Automata
  • 10.3 Discrete Element Method
  • 10.3.1 Particle-Based Simulation of Biological Cells and Tissues
  • 10.3.2 Simulation of Microbial Communities and Their Interactions
  • 10.3.3 Discrete Element Method-Based Modeling of Biological Fluids and Soft Materials
  • 10.4 Smoothed Particle Hydrodynamics
  • 10.4.1 SPH-Based Simulations of Biomimetic Fluid Dynamic
  • 10.4.2 SPH-Based Simulations of Bio-Inspired Engineering Applications
  • Summary
  • Exercise Questions
  • Further Reading
  • 11 Revisiting Cybernetics and Relation to Cybernetical Intelligence
  • 11.1 The Concept and Development of Cybernetics
  • 11.1.1 Attributes of Control Concepts
  • 11.1.2 Research Objects and Characteristics of Cybernetics
  • 11.1.3 Development of Cybernetical Intelligence
  • 11.2 The Fundamental Ideas of Cybernetics
  • 11.2.1 System Idea
  • 11.2.2 Information Idea
  • 11.2.3 Behavioral Idea
  • 11.2.4 Cybernetical Intelligence Neural Network
  • 11.3 Cybernetic Expansion into Other Fields of Research
  • 11.3.1 Social Cybernetics
  • 11.3.2 Internal Control-Related Theories
  • 11.3.3 Software Control Theory
  • 11.3.4 Perceptual Cybernetics
  • 11.4 Practical Application of Cybernetics
  • 11.4.1 Research on the Control Mechanism of Neural Networks
  • 11.4.2 Balance Between Internal Control and Management Power Relations
  • 11.4.3 Software Markov Adaptive Testing Strategy
  • 11.4.4 Task Analysis Model
  • Summary
  • Exer.
  • cise Questions
  • Further Reading
  • 12 Turing Machine
  • 12.1 Behavior of a Turing Machine
  • 12.1.1 Computing with Turing Machines
  • 12.2 Basic Operations of a Turing Machine
  • 12.2.1 Reading and Writing to the Tape
  • 12.2.2 Moving the Tape Head
  • 12.2.3 Changing States
  • 12.3 Interchangeability of Program and Behavior
  • 12.4 Computability Theory
  • 12.4.1 Complexity Theory
  • 12.5 Automata Theory
  • 12.6 Philosophical Issues Related to Turing Machines
  • 12.7 Human and Machine Computations
  • 12.8 Historical Models of Computability
  • 12.9 Recursive Functions
  • 12.10 Turing Machine and Intelligent Control
  • Summary
  • Exercise Questions
  • Further Reading
  • 13 Entropy Concepts in Machine Intelligence
  • 13.1 Relative Entropy of Distributions
  • 13.2 Relative Entropy and Mutual Information
  • 13.3 Entropy in Performance Evaluation
  • 13.4 Cross-Entropy Softmax
  • 13.5 Calculating Cross-Entropy
  • 13.6 Cross-Entropy as a Loss Function
  • 13.7 Cross-Entropy and Log Loss
  • 13.8 Application of Entropy in Intelligent Control
  • 13.8.1 Entropy-Based Control
  • 13.8.2 Fuzzy Entropy
  • 13.8.3 Entropy-Based Control Strategies
  • 13.8.4 Entropy-Based Decision-Making
  • Summary
  • Exercise Questions
  • Further Reading
  • 14 Sampling Methods in Cybernetical Intelligence
  • 14.1 Introduction to Sampling Methods
  • 14.2 Basic Sampling Algorithms
  • 14.2.1 Importance of Sampling Methods in Machine Intelligence
  • 14.3 Machine Learning Sampling Methods
  • 14.3.1 Random Oversampling
  • 14.3.2 Random Undersampling
  • 14.3.3 Synthetic Minority Oversampling Technique
  • 14.3.4 Adaptive Synthetic Sampling
  • 14.4 Advantages and Disadvantages of Machine Learning Sampling Methods
  • 14.5 Advanced Sampling Methods in Cybernetical Intelligence
  • 14.5.1 Ensemble Sampling Method
  • 14.5.2 Active Learning
  • 14.5.3 Bayesian Optimization in Sampling
  • 14.6 Applications of Sampling Methods in Cybernetical Intelligence
  • 14.6.1 Image Processing and Computer Vision
  • 14.6.2 Natural Language Processing
  • 14.6.3 Robotics and Autonomous Systems
  • 14.7 Challenges and Future Directions
  • 14.8 Challenges and Limitations of Sampling Methods
  • 14.9 Emerging Trends and Innovations in Sampling Methods
  • Summary
  • Exercise Questions
  • Further Reading
  • 15 Dynamic System Control
  • 15.1 Linear Systems
  • 15.2 Nonlinear System
  • 15.3 Stability Theory
  • 15.4 Observability and Identification
  • 15.5 Controllability and Stabilizability
  • 15.6 Optimal Control
  • 15.7 Linear Quadratic Regulator Theory
  • 15.8 Time-Optimal Control
  • 15.9 Stochastic Systems with Applications
  • 15.9.1 Stochastic System in Control Systems
  • 15.9.2 Stochastic System in Robotics and Automation
  • 15.9.3 Stochastic System in Neural Networks
  • Summary
  • Exercise Questions
  • Further Reading
  • 16 Deep Learning
  • 16.1 Neural Network Models in Deep Learning
  • 16.2 Methods of Deep Learning
  • 16.2.1 Convolutional Neural Networks
  • 16.2.2 Recurrent Neural Networks
  • 16.2.3 Generative Adversarial Networks
  • 16.2.4 Deep Learning Based Image Segmentation Models
  • 16.2.5 Variational Auto Encoders
  • 16.2.6 Transformer Models
  • 16.2.7 Attention-Based Models
  • 16.2.8 Meta-Learning Models
  • 16.2.9 Capsule Networks
  • 16.3 Deep Learning Frameworks
  • 16.4 Applications of Deep Learning
  • 16.4.1 Object Detection
  • 16.4.2 Intelligent Power Systems
  • 16.4.3 Intelligent Control
  • Summary
  • Exercise Questions
  • References
  • Further Reading
  • 17 Neural Architecture Search
  • 17.1 Neural Architecture Search and Neural Network
  • 17.2 Reinforcement Learning-Based Neural Architecture Search
  • 17.3 Evolutionary Algorithms-Based Neural Architecture Search
  • 17.4 Bayesian Optimization-Based Neural Architecture Search
  • 17.5 Gradient-Based Neural Architecture Search
  • 17.6 One-shot Neural Architecture Search
  • 17.7 Meta-Learning-Based Neural Architecture Search
  • 17.8 Neural Architecture Search for Specific Domains
  • 17.8.1 Cybernetical Intelligent Systems: Neural Architecture Search in Real-World
  • 17.8.2 Neural Architecture Search for Specific Cybernetical Control Tasks
  • 17.8.3 Neural Architecture Search for Cybernetical Intelligent Systems in Real-World
  • 17.8.4 Neural Architecture Search for Adaptive Cybernetical Intelligent Systems
  • 17.9 Comparison of Different Neural Architecture Search Approaches
  • Summary
  • Exercise Questions
  • Further Reading
  • Final Notes on Cybernetical Intelligence
  • Index.

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