{"product_id":"cybernetical-intelligence-engineering-cybernetics-with-machine-intelligence-hardback-9781394217489","title":"Cybernetical Intelligence; Engineering Cybernetics with Machine Intelligence (Hardback) 9781394217489","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eCybernetical Intelligence\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eEngineering Cybernetics with Machine Intelligence\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eKelvin K. L. Wong (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394217489, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 16 October 2023\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e432 pages\u003cbr\u003e22.9 x 15.2 x 2.6 cm, 0.844 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cb\u003eCYBERNETICAL INTELLIGENCE\u003c\/b\u003e \u003cp\u003e\u003cb\u003eHighly comprehensive, detailed, and up-to-date overview of artificial intelligence and cybernetics, with practical examples and supplementary learning resources\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003e\u003ci\u003eCybernetical Intelligence: Engineering Cybernetics with Machine Intelligence \u003c\/i\u003eis 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 “\u003ci\u003eCybernetical Intelligence\u003c\/i\u003e” 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. \u003c\/p\u003e\n\u003cp\u003eEach 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. \u003c\/p\u003e\n\u003cp\u003e\u003ci\u003eCybernetical Intelligence \u003c\/i\u003eincludes information on: \u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe history and development of cybernetics and its influence on the development of neural networks\u003c\/li\u003e \u003cli\u003eDevelopments and innovations in artificial intelligence and machine learning, such as deep reinforcement learning, generative adversarial networks, and transfer learning\u003c\/li\u003e \u003cli\u003eMathematical foundations of artificial intelligence and cybernetics, including linear algebra, calculus, and probability theory\u003c\/li\u003e \u003cli\u003eEthical implications of artificial intelligence and cybernetics as well as responsible and transparent development and deployment of AI systems\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003ePresenting a highly detailed and comprehensive overview of the field, with modern developments thoroughly discussed, \u003ci\u003eCybernetical Intelligence \u003c\/i\u003eis 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.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xv\u003c\/p\u003e \u003cp\u003eAbout the Author xix\u003c\/p\u003e \u003cp\u003eAbout the Companion Website xxi\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Artificial Intelligence and Cybernetical Learning 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Artificial Intelligence Initiative 1\u003c\/p\u003e \u003cp\u003e1.2 Intelligent Automation Initiative 4\u003c\/p\u003e \u003cp\u003e1.2.1 Benefits of IAI 5\u003c\/p\u003e \u003cp\u003e1.3 Artificial Intelligence Versus Intelligent Automation 5\u003c\/p\u003e \u003cp\u003e1.3.1 Process Discovery 6\u003c\/p\u003e \u003cp\u003e1.3.2 Optimization 7\u003c\/p\u003e \u003cp\u003e1.3.3 Analytics and Insight 8\u003c\/p\u003e \u003cp\u003e1.4 The Fourth Industrial Revolution and Artificial Intelligence 9\u003c\/p\u003e \u003cp\u003e1.4.1 Artificial Narrow Intelligence 10\u003c\/p\u003e \u003cp\u003e1.4.2 Artificial General Intelligence 12\u003c\/p\u003e \u003cp\u003e1.4.3 Artificial Super Intelligence 13\u003c\/p\u003e \u003cp\u003e1.5 Pattern Analysis and Cognitive Learning 14\u003c\/p\u003e \u003cp\u003e1.5.1 Machine Learning 15\u003c\/p\u003e \u003cp\u003e1.5.1.1 Parametric Algorithms 16\u003c\/p\u003e \u003cp\u003e1.5.1.2 Nonparametric Algorithms 17\u003c\/p\u003e \u003cp\u003e1.5.2 Deep Learning 20\u003c\/p\u003e \u003cp\u003e1.5.2.1 Convolutional Neural Networks in Advancing Artificial Intelligence 21\u003c\/p\u003e \u003cp\u003e1.5.2.2 Future Advancement in Deep Learning 22\u003c\/p\u003e \u003cp\u003e1.5.3 Cybernetical Learning 23\u003c\/p\u003e \u003cp\u003e1.6 Cybernetical Artificial Intelligence 24\u003c\/p\u003e \u003cp\u003e1.6.1 Artificial Intelligence Control Theory 24\u003c\/p\u003e \u003cp\u003e1.6.2 Information Theory 26\u003c\/p\u003e \u003cp\u003e1.6.3 Cybernetic Systems 27\u003c\/p\u003e \u003cp\u003e1.7 Cybernetical Intelligence Definition 28\u003c\/p\u003e \u003cp\u003e1.8 The Future of Cybernetical Intelligence 30\u003c\/p\u003e \u003cp\u003eSummary 32\u003c\/p\u003e \u003cp\u003eExercise Questions 32\u003c\/p\u003e \u003cp\u003eFurther Reading 33\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Cybernetical Intelligent Control 35\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Control Theory and Feedback Control Systems 35\u003c\/p\u003e \u003cp\u003e2.2 Maxwell’s Analysis of Governors 37\u003c\/p\u003e \u003cp\u003e2.3 Harold Black 39\u003c\/p\u003e \u003cp\u003e2.4 Nyquist and Bode 40\u003c\/p\u003e \u003cp\u003e2.5 Stafford Beer 42\u003c\/p\u003e \u003cp\u003e2.5.1 Cybernetic Control 42\u003c\/p\u003e \u003cp\u003e2.5.2 Viable Systems Model 42\u003c\/p\u003e \u003cp\u003e2.5.3 Cybernetics Models of Management 43\u003c\/p\u003e \u003cp\u003e2.6 James Lovelock 43\u003c\/p\u003e \u003cp\u003e2.6.1 Cybernetic Approach to Ecosystems 43\u003c\/p\u003e \u003cp\u003e2.6.2 Gaia Hypothesis 44\u003c\/p\u003e \u003cp\u003e2.7 Macy Conference 44\u003c\/p\u003e \u003cp\u003e2.8 McCulloch–Pitts 45\u003c\/p\u003e \u003cp\u003e2.9 John von Neumann 47\u003c\/p\u003e \u003cp\u003e2.9.1 Discussions on Self-Replicating Machines 47\u003c\/p\u003e \u003cp\u003e2.9.2 Discussions on Machine Learning 48\u003c\/p\u003e \u003cp\u003eSummary 48\u003c\/p\u003e \u003cp\u003eExercise Questions 49\u003c\/p\u003e \u003cp\u003eFurther Reading 50\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 The Basics of Perceptron 51\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 The Analogy of Biological and Artificial Neurons 51\u003c\/p\u003e \u003cp\u003e3.1.1 Biological Neurons and Neurodynamics 52\u003c\/p\u003e \u003cp\u003e3.1.2 The Structure of Neural Network 53\u003c\/p\u003e \u003cp\u003e3.1.3 Encoding and Decoding 56\u003c\/p\u003e \u003cp\u003e3.2 Perception and Multilayer Perceptron 57\u003c\/p\u003e \u003cp\u003e3.2.1 Back Propagation Neural Network 59\u003c\/p\u003e \u003cp\u003e3.2.2 Derivative Equations for Backpropagation 59\u003c\/p\u003e \u003cp\u003e3.3 Activation Function 61\u003c\/p\u003e \u003cp\u003e3.3.1 Sigmoid Activation Function 61\u003c\/p\u003e \u003cp\u003e3.3.2 Hyperbolic Tangent Activation Function 62\u003c\/p\u003e \u003cp\u003e3.3.3 Rectified Linear Unit Activation Function 62\u003c\/p\u003e \u003cp\u003e3.3.4 Linear Activation Function 64\u003c\/p\u003e \u003cp\u003eSummary 65\u003c\/p\u003e \u003cp\u003eExercise Questions 67\u003c\/p\u003e \u003cp\u003eFurther Reading 67\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 The Structure of Neural Network 69\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Layers in Neural Network 69\u003c\/p\u003e \u003cp\u003e4.1.1 Input Layer 69\u003c\/p\u003e \u003cp\u003e4.1.2 Hidden Layer 70\u003c\/p\u003e \u003cp\u003e4.1.3 Neurons 70\u003c\/p\u003e \u003cp\u003e4.1.4 Weights and Biases 71\u003c\/p\u003e \u003cp\u003e4.1.5 Forward Propagation 72\u003c\/p\u003e \u003cp\u003e4.1.6 Backpropagation 72\u003c\/p\u003e \u003cp\u003e4.2 Perceptron and Multilayer Perceptron 73\u003c\/p\u003e \u003cp\u003e4.3 Recurrent Neural Network 75\u003c\/p\u003e \u003cp\u003e4.3.1 Long Short-Term Memory 76\u003c\/p\u003e \u003cp\u003e4.4 Markov Neural Networks 77\u003c\/p\u003e \u003cp\u003e4.4.1 State Transition Function 77\u003c\/p\u003e \u003cp\u003e4.4.2 Observation Function 78\u003c\/p\u003e \u003cp\u003e4.4.3 Policy Function 78\u003c\/p\u003e \u003cp\u003e4.4.4 Loss Function 78\u003c\/p\u003e \u003cp\u003e4.5 Generative Adversarial Network 78\u003c\/p\u003e \u003cp\u003eSummary 79\u003c\/p\u003e \u003cp\u003eExercise Questions 80\u003c\/p\u003e \u003cp\u003eFurther Reading 81\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Backpropagation Neural Network 83\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Backpropagation Neural Network 83\u003c\/p\u003e \u003cp\u003e5.1.1 Forward Propagation 85\u003c\/p\u003e \u003cp\u003e5.2 Gradient Descent 85\u003c\/p\u003e \u003cp\u003e5.2.1 Loss Function 85\u003c\/p\u003e \u003cp\u003e5.2.2 Parameters in Gradient Descent 88\u003c\/p\u003e \u003cp\u003e5.2.3 Gradient in Gradient Descent 88\u003c\/p\u003e \u003cp\u003e5.2.4 Learning Rate in Gradient Descent 89\u003c\/p\u003e \u003cp\u003e5.2.5 Update Rule in Gradient Descent 89\u003c\/p\u003e \u003cp\u003e5.3 Stopping Criteria 89\u003c\/p\u003e \u003cp\u003e5.3.1 Convergence and Stopping Criteria 90\u003c\/p\u003e \u003cp\u003e5.3.2 Local Minimum and Global Minimum 91\u003c\/p\u003e \u003cp\u003e5.4 Resampling Methods 91\u003c\/p\u003e \u003cp\u003e5.4.1 Cross-Validation 93\u003c\/p\u003e \u003cp\u003e5.4.2 Bootstrapping 93\u003c\/p\u003e \u003cp\u003e5.4.3 Monte Carlo Cross-Validation 94\u003c\/p\u003e \u003cp\u003e5.5 Optimizers in Neural Network 94\u003c\/p\u003e \u003cp\u003e5.5.1 Stochastic Gradient Descent 94\u003c\/p\u003e \u003cp\u003e5.5.2 Root Mean Square Propagation 96\u003c\/p\u003e \u003cp\u003e5.5.3 Adaptive Moment Estimation 96\u003c\/p\u003e \u003cp\u003e5.5.4 AdaMax 97\u003c\/p\u003e \u003cp\u003e5.5.5 Momentum Optimization 97\u003c\/p\u003e \u003cp\u003eSummary 97\u003c\/p\u003e \u003cp\u003eExercise Questions 99\u003c\/p\u003e \u003cp\u003eFurther Reading 100\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Application of Neural Network in Learning and Recognition 101\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Applying Backpropagation to Shape Recognition 101\u003c\/p\u003e \u003cp\u003e6.2 Softmax Regression 105\u003c\/p\u003e \u003cp\u003e6.3 K-Binary Classifier 107\u003c\/p\u003e \u003cp\u003e6.4 Relational Learning via Neural Network 108\u003c\/p\u003e \u003cp\u003e6.4.1 Graph Neural Network 109\u003c\/p\u003e \u003cp\u003e6.4.2 Graph Convolutional Network 111\u003c\/p\u003e \u003cp\u003e6.5 Cybernetics Using Neural Network 112\u003c\/p\u003e \u003cp\u003e6.6 Structure of Neural Network for Image Processing 115\u003c\/p\u003e \u003cp\u003e6.7 Transformer Networks 116\u003c\/p\u003e \u003cp\u003e6.8 Attention Mechanisms 116\u003c\/p\u003e \u003cp\u003e6.9 Graph Neural Networks 117\u003c\/p\u003e \u003cp\u003e6.10 Transfer Learning 118\u003c\/p\u003e \u003cp\u003e6.11 Generalization of Neural Networks 119\u003c\/p\u003e \u003cp\u003e6.12 Performance Measures 120\u003c\/p\u003e \u003cp\u003e6.12.1 Confusion Matrix 120\u003c\/p\u003e \u003cp\u003e6.12.2 Receiver Operating Characteristic 121\u003c\/p\u003e \u003cp\u003e6.12.3 Area Under the ROC Curve 122\u003c\/p\u003e \u003cp\u003eSummary 123\u003c\/p\u003e \u003cp\u003eExercise Questions 123\u003c\/p\u003e \u003cp\u003eFurther Reading 124\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Competitive Learning and Self-Organizing Map 125\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Principal of Competitive Learning 125\u003c\/p\u003e \u003cp\u003e7.1.1 Step 1: Normalized Input Vector 128\u003c\/p\u003e \u003cp\u003e7.1.2 Step 2: Find the Winning Neuron 128\u003c\/p\u003e \u003cp\u003e7.1.3 Step 3: Adjust the Network Weight Vector and Output Results 129\u003c\/p\u003e \u003cp\u003e7.2 Basic Structure of Self-Organizing Map 129\u003c\/p\u003e \u003cp\u003e7.2.1 Properties Self-Organizing Map 130\u003c\/p\u003e \u003cp\u003e7.3 Self-Organizing Mapping Neural Network Algorithm 131\u003c\/p\u003e \u003cp\u003e7.3.1 Step 1: Initialize Parameter 132\u003c\/p\u003e \u003cp\u003e7.3.2 Step 2: Select Inputs and Determine Winning Nodes 132\u003c\/p\u003e \u003cp\u003e7.3.3 Step 3: Affect Neighboring Neurons 132\u003c\/p\u003e \u003cp\u003e7.3.4 Step 4: Adjust Weights 133\u003c\/p\u003e \u003cp\u003e7.3.5 Step 5: Judging the End Condition 133\u003c\/p\u003e \u003cp\u003e7.4 Growing Self-Organizing Map 133\u003c\/p\u003e \u003cp\u003e7.5 Time Adaptive Self-Organizing Map 136\u003c\/p\u003e \u003cp\u003e7.5.1 TASOM-Based Algorithms for Real Applications 138\u003c\/p\u003e \u003cp\u003e7.6 Oriented and Scalable Map 139\u003c\/p\u003e \u003cp\u003e7.7 Generative Topographic Map 141\u003c\/p\u003e \u003cp\u003eSummary 145\u003c\/p\u003e \u003cp\u003eExercise Questions 146\u003c\/p\u003e \u003cp\u003eFurther Reading 147\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Support Vector Machine 149\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 The Definition of Data Clustering 149\u003c\/p\u003e \u003cp\u003e8.2 Support Vector and Margin 152\u003c\/p\u003e \u003cp\u003e8.3 Kernel Function 155\u003c\/p\u003e \u003cp\u003e8.3.1 Linear Kernel 155\u003c\/p\u003e \u003cp\u003e8.3.2 Polynomial Kernel 156\u003c\/p\u003e \u003cp\u003e8.3.3 Radial Basis Function 157\u003c\/p\u003e \u003cp\u003e8.3.4 Laplace Kernel 159\u003c\/p\u003e \u003cp\u003e8.3.5 Sigmoid Kernel 159\u003c\/p\u003e \u003cp\u003e8.4 Linear and Nonlinear Support Vector Machine 160\u003c\/p\u003e \u003cp\u003e8.5 Hard Margin and Soft Margin in Support Vector Machine 164\u003c\/p\u003e \u003cp\u003e8.6 I\/O of Support Vector Machine 167\u003c\/p\u003e \u003cp\u003e8.6.1 Training Data 167\u003c\/p\u003e \u003cp\u003e8.6.2 Feature Matrix and Label Vector 168\u003c\/p\u003e \u003cp\u003e8.7 Hyperparameters of Support Vector Machine 169\u003c\/p\u003e \u003cp\u003e8.7.1 The C Hyperparameter 169\u003c\/p\u003e \u003cp\u003e8.7.2 Kernel Coefficient 169\u003c\/p\u003e \u003cp\u003e8.7.3 Class Weights 170\u003c\/p\u003e \u003cp\u003e8.7.4 Convergence Criteria 170\u003c\/p\u003e \u003cp\u003e8.7.5 Regularization 171\u003c\/p\u003e \u003cp\u003e8.8 Application of Support Vector Machine 171\u003c\/p\u003e \u003cp\u003e8.8.1 Classification 171\u003c\/p\u003e \u003cp\u003e8.8.2 Regression 173\u003c\/p\u003e \u003cp\u003e8.8.3 Image Classification 173\u003c\/p\u003e \u003cp\u003e8.8.4 Text Classification 174\u003c\/p\u003e \u003cp\u003eSummary 174\u003c\/p\u003e \u003cp\u003eExercise Questions 175\u003c\/p\u003e \u003cp\u003eFurther Reading 176\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Bio-Inspired Cybernetical Intelligence 177\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Genetic Algorithm 178\u003c\/p\u003e \u003cp\u003e9.2 Ant Colony Optimization 181\u003c\/p\u003e \u003cp\u003e9.3 Bees Algorithm 184\u003c\/p\u003e \u003cp\u003e9.4 Artificial Bee Colony Algorithm 186\u003c\/p\u003e \u003cp\u003e9.5 Cuckoo Search 189\u003c\/p\u003e \u003cp\u003e9.6 Particle Swarm Optimization 193\u003c\/p\u003e \u003cp\u003e9.7 Bacterial Foraging Optimization 196\u003c\/p\u003e \u003cp\u003e9.8 Gray Wolf Optimizer 197\u003c\/p\u003e \u003cp\u003e9.9 Firefly Algorithm 199\u003c\/p\u003e \u003cp\u003eSummary 200\u003c\/p\u003e \u003cp\u003eExercise Questions 201\u003c\/p\u003e \u003cp\u003eFurther Reading 202\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Life-Inspired Machine Intelligence and Cybernetics 203\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Multi-Agent AI Systems 203\u003c\/p\u003e \u003cp\u003e10.1.1 Game Theory 205\u003c\/p\u003e \u003cp\u003e10.1.2 Distributed Multi-Agent Systems 206\u003c\/p\u003e \u003cp\u003e10.1.3 Multi-Agent Reinforcement Learning 207\u003c\/p\u003e \u003cp\u003e10.1.4 Evolutionary Computation and Multi-Agent Systems 209\u003c\/p\u003e \u003cp\u003e10.2 Cellular Automata 211\u003c\/p\u003e \u003cp\u003e10.3 Discrete Element Method 212\u003c\/p\u003e \u003cp\u003e10.3.1 Particle-Based Simulation of Biological Cells and Tissues 214\u003c\/p\u003e \u003cp\u003e10.3.2 Simulation of Microbial Communities and Their Interactions 215\u003c\/p\u003e \u003cp\u003e10.3.3 Discrete Element Method-Based Modeling of Biological Fluids and Soft Materials 216\u003c\/p\u003e \u003cp\u003e10.4 Smoothed Particle Hydrodynamics 218\u003c\/p\u003e \u003cp\u003e10.4.1 SPH-Based Simulations of Biomimetic Fluid Dynamic 219\u003c\/p\u003e \u003cp\u003e10.4.2 SPH-Based Simulations of Bio-Inspired Engineering Applications 220\u003c\/p\u003e \u003cp\u003eSummary 221\u003c\/p\u003e \u003cp\u003eExercise Questions 222\u003c\/p\u003e \u003cp\u003eFurther Reading 223\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Revisiting Cybernetics and Relation to Cybernetical Intelligence 225\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 The Concept and Development of Cybernetics 225\u003c\/p\u003e \u003cp\u003e11.1.1 Attributes of Control Concepts 225\u003c\/p\u003e \u003cp\u003e11.1.2 Research Objects and Characteristics of Cybernetics 226\u003c\/p\u003e \u003cp\u003e11.1.3 Development of Cybernetical Intelligence 227\u003c\/p\u003e \u003cp\u003e11.2 The Fundamental Ideas of Cybernetics 227\u003c\/p\u003e \u003cp\u003e11.2.1 System Idea 227\u003c\/p\u003e \u003cp\u003e11.2.2 Information Idea 229\u003c\/p\u003e \u003cp\u003e11.2.3 Behavioral Idea 230\u003c\/p\u003e \u003cp\u003e11.2.4 Cybernetical Intelligence Neural Network 231\u003c\/p\u003e \u003cp\u003e11.3 Cybernetic Expansion into Other Fields of Research 234\u003c\/p\u003e \u003cp\u003e11.3.1 Social Cybernetics 234\u003c\/p\u003e \u003cp\u003e11.3.2 Internal Control-Related Theories 237\u003c\/p\u003e \u003cp\u003e11.3.3 Software Control Theory 237\u003c\/p\u003e \u003cp\u003e11.3.4 Perceptual Cybernetics 238\u003c\/p\u003e \u003cp\u003e11.4 Practical Application of Cybernetics 240\u003c\/p\u003e \u003cp\u003e11.4.1 Research on the Control Mechanism of Neural Networks 240\u003c\/p\u003e \u003cp\u003e11.4.2 Balance Between Internal Control and Management Power Relations 240\u003c\/p\u003e \u003cp\u003e11.4.3 Software Markov Adaptive Testing Strategy 242\u003c\/p\u003e \u003cp\u003e11.4.4 Task Analysis Model 244\u003c\/p\u003e \u003cp\u003eSummary 245\u003c\/p\u003e \u003cp\u003eExercise Questions 246\u003c\/p\u003e \u003cp\u003eFurther Reading 247\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Turing Machine 249\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Behavior of a Turing Machine 250\u003c\/p\u003e \u003cp\u003e12.1.1 Computing with Turing Machines 251\u003c\/p\u003e \u003cp\u003e12.2 Basic Operations of a Turing Machine 252\u003c\/p\u003e \u003cp\u003e12.2.1 Reading and Writing to the Tape 253\u003c\/p\u003e \u003cp\u003e12.2.2 Moving the Tape Head 254\u003c\/p\u003e \u003cp\u003e12.2.3 Changing States 254\u003c\/p\u003e \u003cp\u003e12.3 Interchangeability of Program and Behavior 255\u003c\/p\u003e \u003cp\u003e12.4 Computability Theory 256\u003c\/p\u003e \u003cp\u003e12.4.1 Complexity Theory 257\u003c\/p\u003e \u003cp\u003e12.5 Automata Theory 258\u003c\/p\u003e \u003cp\u003e12.6 Philosophical Issues Related to Turing Machines 259\u003c\/p\u003e \u003cp\u003e12.7 Human and Machine Computations 260\u003c\/p\u003e \u003cp\u003e12.8 Historical Models of Computability 261\u003c\/p\u003e \u003cp\u003e12.9 Recursive Functions 262\u003c\/p\u003e \u003cp\u003e12.10 Turing Machine and Intelligent Control 263\u003c\/p\u003e \u003cp\u003eSummary 264\u003c\/p\u003e \u003cp\u003eExercise Questions 265\u003c\/p\u003e \u003cp\u003eFurther Reading 265\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Entropy Concepts in Machine Intelligence 267\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Relative Entropy of Distributions 268\u003c\/p\u003e \u003cp\u003e13.2 Relative Entropy and Mutual Information 268\u003c\/p\u003e \u003cp\u003e13.3 Entropy in Performance Evaluation 269\u003c\/p\u003e \u003cp\u003e13.4 Cross-Entropy Softmax 271\u003c\/p\u003e \u003cp\u003e13.5 Calculating Cross-Entropy 272\u003c\/p\u003e \u003cp\u003e13.6 Cross-Entropy as a Loss Function 273\u003c\/p\u003e \u003cp\u003e13.7 Cross-Entropy and Log Loss 274\u003c\/p\u003e \u003cp\u003e13.8 Application of Entropy in Intelligent Control 275\u003c\/p\u003e \u003cp\u003e13.8.1 Entropy-Based Control 275\u003c\/p\u003e \u003cp\u003e13.8.2 Fuzzy Entropy 276\u003c\/p\u003e \u003cp\u003e13.8.3 Entropy-Based Control Strategies 277\u003c\/p\u003e \u003cp\u003e13.8.4 Entropy-Based Decision-Making 278\u003c\/p\u003e \u003cp\u003eSummary 279\u003c\/p\u003e \u003cp\u003eExercise Questions 279\u003c\/p\u003e \u003cp\u003eFurther Reading 280\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Sampling Methods in Cybernetical Intelligence 283\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction to Sampling Methods 283\u003c\/p\u003e \u003cp\u003e14.2 Basic Sampling Algorithms 284\u003c\/p\u003e \u003cp\u003e14.2.1 Importance of Sampling Methods in Machine Intelligence 286\u003c\/p\u003e \u003cp\u003e14.3 Machine Learning Sampling Methods 287\u003c\/p\u003e \u003cp\u003e14.3.1 Random Oversampling 288\u003c\/p\u003e \u003cp\u003e14.3.2 Random Undersampling 290\u003c\/p\u003e \u003cp\u003e14.3.3 Synthetic Minority Oversampling Technique 290\u003c\/p\u003e \u003cp\u003e14.3.4 Adaptive Synthetic Sampling 292\u003c\/p\u003e \u003cp\u003e14.4 Advantages and Disadvantages of Machine Learning Sampling Methods 293\u003c\/p\u003e \u003cp\u003e14.5 Advanced Sampling Methods in Cybernetical Intelligence 294\u003c\/p\u003e \u003cp\u003e14.5.1 Ensemble Sampling Method 295\u003c\/p\u003e \u003cp\u003e14.5.2 Active Learning 297\u003c\/p\u003e \u003cp\u003e14.5.3 Bayesian Optimization in Sampling 299\u003c\/p\u003e \u003cp\u003e14.6 Applications of Sampling Methods in Cybernetical Intelligence 302\u003c\/p\u003e \u003cp\u003e14.6.1 Image Processing and Computer Vision 302\u003c\/p\u003e \u003cp\u003e14.6.2 Natural Language Processing 304\u003c\/p\u003e \u003cp\u003e14.6.3 Robotics and Autonomous Systems 307\u003c\/p\u003e \u003cp\u003e14.7 Challenges and Future Directions 308\u003c\/p\u003e \u003cp\u003e14.8 Challenges and Limitations of Sampling Methods 309\u003c\/p\u003e \u003cp\u003e14.9 Emerging Trends and Innovations in Sampling Methods 309\u003c\/p\u003e \u003cp\u003eSummary 310\u003c\/p\u003e \u003cp\u003eExercise Questions 311\u003c\/p\u003e \u003cp\u003eFurther Reading 312\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Dynamic System Control 313\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Linear Systems 314\u003c\/p\u003e \u003cp\u003e15.2 Nonlinear System 316\u003c\/p\u003e \u003cp\u003e15.3 Stability Theory 318\u003c\/p\u003e \u003cp\u003e15.4 Observability and Identification 320\u003c\/p\u003e \u003cp\u003e15.5 Controllability and Stabilizability 321\u003c\/p\u003e \u003cp\u003e15.6 Optimal Control 323\u003c\/p\u003e \u003cp\u003e15.7 Linear Quadratic Regulator Theory 324\u003c\/p\u003e \u003cp\u003e15.8 Time-Optimal Control 326\u003c\/p\u003e \u003cp\u003e15.9 Stochastic Systems with Applications 328\u003c\/p\u003e \u003cp\u003e15.9.1 Stochastic System in Control Systems 329\u003c\/p\u003e \u003cp\u003e15.9.2 Stochastic System in Robotics and Automation 329\u003c\/p\u003e \u003cp\u003e15.9.3 Stochastic System in Neural Networks 330\u003c\/p\u003e \u003cp\u003eSummary 331\u003c\/p\u003e \u003cp\u003eExercise Questions 331\u003c\/p\u003e \u003cp\u003eFurther Reading 332\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Deep Learning 333\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 Neural Network Models in Deep Learning 335\u003c\/p\u003e \u003cp\u003e16.2 Methods of Deep Learning 336\u003c\/p\u003e \u003cp\u003e16.2.1 Convolutional Neural Networks 337\u003c\/p\u003e \u003cp\u003e16.2.2 Recurrent Neural Networks 340\u003c\/p\u003e \u003cp\u003e16.2.3 Generative Adversarial Networks 342\u003c\/p\u003e \u003cp\u003e16.2.4 Deep Learning Based Image Segmentation Models 345\u003c\/p\u003e \u003cp\u003e16.2.5 Variational Auto Encoders 348\u003c\/p\u003e \u003cp\u003e16.2.6 Transformer Models 350\u003c\/p\u003e \u003cp\u003e16.2.7 Attention-Based Models 352\u003c\/p\u003e \u003cp\u003e16.2.8 Meta-Learning Models 354\u003c\/p\u003e \u003cp\u003e16.2.9 Capsule Networks 357\u003c\/p\u003e \u003cp\u003e16.3 Deep Learning Frameworks 358\u003c\/p\u003e \u003cp\u003e16.4 Applications of Deep Learning 359\u003c\/p\u003e \u003cp\u003e16.4.1 Object Detection 360\u003c\/p\u003e \u003cp\u003e16.4.2 Intelligent Power Systems 361\u003c\/p\u003e \u003cp\u003e16.4.3 Intelligent Control 362\u003c\/p\u003e \u003cp\u003eSummary 362\u003c\/p\u003e \u003cp\u003eExercise Questions 363\u003c\/p\u003e \u003cp\u003eReferences 364\u003c\/p\u003e \u003cp\u003eFurther Reading 365\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Neural Architecture Search 367\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e17.1 Neural Architecture Search and Neural Network 369\u003c\/p\u003e \u003cp\u003e17.2 Reinforcement Learning-Based Neural Architecture Search 371\u003c\/p\u003e \u003cp\u003e17.3 Evolutionary Algorithms-Based Neural Architecture Search 374\u003c\/p\u003e \u003cp\u003e17.4 Bayesian Optimization-Based Neural Architecture Search 376\u003c\/p\u003e \u003cp\u003e17.5 Gradient-Based Neural Architecture Search 378\u003c\/p\u003e \u003cp\u003e17.6 One-shot Neural Architecture Search 379\u003c\/p\u003e \u003cp\u003e17.7 Meta-Learning-Based Neural Architecture Search 381\u003c\/p\u003e \u003cp\u003e17.8 Neural Architecture Search for Specific Domains 383\u003c\/p\u003e \u003cp\u003e17.8.1 Cybernetical Intelligent Systems: Neural Architecture Search in Real-World 384\u003c\/p\u003e \u003cp\u003e17.8.2 Neural Architecture Search for Specific Cybernetical Control Tasks 385\u003c\/p\u003e \u003cp\u003e17.8.3 Neural Architecture Search for Cybernetical Intelligent Systems in Real-World 386\u003c\/p\u003e \u003cp\u003e17.8.4 Neural Architecture Search for Adaptive Cybernetical Intelligent Systems 388\u003c\/p\u003e \u003cp\u003e17.9 Comparison of Different Neural Architecture Search Approaches 389\u003c\/p\u003e \u003cp\u003eSummary 391\u003c\/p\u003e \u003cp\u003eExercise Questions 391\u003c\/p\u003e \u003cp\u003eFurther Reading 392\u003c\/p\u003e \u003cp\u003eFinal Notes on \u003ci\u003eCybernetical Intelligence\u003c\/i\u003e 393\u003c\/p\u003e \u003cp\u003eIndex 399\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Computer science [\u003ca title=\"See our other books on Computer science\" 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