Cybernetical intelligence : engineering cybernetics with machine intelligence / Kelvin K. L. Wong.
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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Full Text (via IEEE) |
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| Main Author: | |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Hoboken, NJ :
Wiley : IEEE Press,
[2024]
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| Subjects: |
MARC
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| 245 | 1 | 0 | |a Cybernetical intelligence : |b engineering cybernetics with machine intelligence / |c Kelvin K. L. Wong. |
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| 505 | 0 | |8 1.1 x |a 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. | |
| 505 | 8 | |8 1.2 x |a 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. | |
| 520 | |a 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 field of cybernetics and neural networks, as well as the mathematical foundations of these technologies. The book provides a detailed explanation of various types of neural networks, including feedforward networks, recurrent neural networks, and convolutional neural networks as well as their applications to different real-world problems. This groundbreaking book presents a pioneering exploration of machine learning within the framework of cybernetics. It marks a significant milestone in the field's history, as it is the first book to describe the development of machine learning from a cybernetics perspective. The introduction of the concept of "Cybernetical Intelligence" and the generation of new terminology within this context propel new lines of thought in the historical development of artificial intelligence. With its profound implications and contributions, this book holds immense importance and is poised to become a definitive resource for scholars and researchers in this field of study. Each chapter is specifically designed to introduce the theory with several examples. This comprehensive book includes exercise questions at the end of each chapter, providing readers with valuable opportunities to apply and strengthen their understanding of cybernetical intelligence. To further support the learning journey, solutions to these questions are readily accessible on the book's companion site. Additionally, the companion site offers programming practice exercises and assignments, enabling readers to delve deeper into the practical aspects of the subject matter. Cybernetical Intelligence includes information on: The history and development of cybernetics and its influence on the development of neural networks Developments and innovations in artificial intelligence and machine learning, such as deep reinforcement learning, generative adversarial networks, and transfer learning Mathematical foundations of artificial intelligence and cybernetics, including linear algebra, calculus, and probability theory Ethical implications of artificial intelligence and cybernetics as well as responsible and transparent development and deployment of AI systems Presenting a highly detailed and comprehensive overview of the field, with modern developments thoroughly discussed, Cybernetical Intelligence is an essential textbook that helps students make connections with real-life engineering problems by providing both theory and practice, along with a myriad of helpful learning aids. | ||
| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Online resource; title from PDF title page (John Wiley, viewed October 23, 2023). | |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Cybernetics. | |
| 650 | 0 | |a Neural networks (Computer science) | |
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| 650 | 7 | |a Machine learning. |2 fast |0 (OCoLC)fst01004795 | |
| 650 | 7 | |a Neural networks (Computer science) |2 fast |0 (OCoLC)fst01036260 | |
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