Cybernetical Intelligence

Name: Cybernetical Intelligence | Engineering Cybernetics with Machine Intelligence
Brand: Wiley
Price: 115.99 EUR
Availability: OnlineOnly

Engineering Cybernetics with Machine Intelligence

Kelvin K. L. Wong(Author)

Wiley (Publisher)

1st Edition

Published on 11. October 2023

432 pages

E-Book

PDF with Adobe-DRM

System requirements

978-1-394-21749-6 (ISBN)

€115.99incl. 7% vat

System requirements

for PDF with Adobe-DRM

E-Book Single Licence

Available for download

Description

Alles über E-Books | Antworten auf Fragen rund um E-Books, Kopierschutz und Dateiformate finden Sie in unserem Info- & Hilfebereich.

Alles über E-Books, Kopierschutz & Dateiformate finden Sie in unserem Info- & Hilfebereich.

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.

More details

Other editions

Person

Content

Cover
Title Page
Copyright Page
Contents
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Exercise Questions
Further Reading
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
EULA

System requirements

Save as PDF Copy link into clipboard

Schweitzer Fachinformationen

Cybernetical Intelligence

Description

More details

Other editions

Additional editions

Person

Content

System requirements