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

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.…”
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Table of Contents: “…Elements of the Kalman Filter -- 3.1.2. Steps of the Kalman Filter -- 3.1.3. Kalman Filter Applications -- 3.2. …”
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