The quest to create machines that can solve problems as humans do leads us to intelligent control. This field encompasses control systems that can adapt to changes and learn to improve their actions—traits typically associated with human intelligence. In this work we seek to determine how intelligent these classes of control systems are by quantifying their level of adaptability and learning. First we describe the stages of development towards intelligent control and present a definition based on literature. Based on the key elements of this definition, we propose a novel taxonomy of intelligent control methods, which assesses the extent to which they handle uncertainties in three areas: the environment, the controller, and the goals. This taxonomy is applicable to a variety of robotic and other autonomous systems, which we demonstrate through several examples of intelligent control methods and their classifications. Looking at the spread of classifications based on this taxonomy can help researchers identify where control systems can be made more intelligent.

What Is Intelligent Control The term "intelligent control" refers to a category of control methods that make use of a number of different artificial intelligence computing methodologies, including neural networks, Bayesian probability, fuzzy logic, machine learning, reinforcement learning, evolutionary computation, and genetic algorithms. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Intelligent Control Chapter 2: Artificial Intelligence Chapter 3: Machine Learning Chapter 4: Reinforcement Learning Chapter 5: Neural Network Chapter 6: Adaptive Control Chapter 7: Computational Intelligence Chapter 8: Outline of Artificial Intelligence Chapter 9: Machine Learning Control Chapter 10: Data-driven Model (II) Answering the public top questions about intelligent control. (III) Real world examples for the usage of intelligent control in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of intelligent control' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of intelligent control.

What is intelligence? Are truly intelligent machines a practical reality? If so, can they work in harmony with human beings and improve the quality of our lives? How are they designed, built, and controlled? The fact is that machines with brains are no longer the stuff of science fiction. Research focused on developing smarter, more flexible

This book presents the proceedings of the 4th International Conference on Advanced Intelligent Systems and Informatics 2018 (AISI2018), which took place in Cairo, Egypt from September 1 to 3, 2018. This international and interdisciplinary conference, which highlighted essential research and developments in the field of informatics and intelligent systems, was organized by the Scientific Research Group in Egypt (SRGE). The book is divided into several main sections: Intelligent Systems; Robot Modeling and Control Systems; Intelligent Robotics Systems; Machine Learning Methodology and Applications; Sentiment Analysis and Arabic Text Mining; Swarm Optimizations and Applications; Deep Learning and Cloud Computing; Information Security, Hiding, and Biometric Recognition; and Data Mining, Visualization and E-learning.

This book constitutes the refereed proceedings of the 9th International Conference on Bioinspired Optimization Methods and Their Applications, BIOMA 2020, held in Brussels, Belgium, in November 2020. The 24 full papers presented in this book were carefully reviewed and selected from 68 submissions. The papers in this BIOMA proceedings specialized in bioinspired algorithms as a means for solving the optimization problems and came in two categories: theoretical studies and methodology advancements on the one hand, and algorithm adjustments and their applications on the other. Due to the Corona pandemic BIOMA 2020 was held as a virtual event.

This is an open access book. As on date, huge volumes of data are being generated through sensors, satellites, and simulators. Modern research on data analytics and its applications reveal that several algorithms are being designed and developed to process these datasets, either through the use of sequential and parallel processes. In the current scenario of Industry 4.0, data analytics, artificial intelligence and machine learning are being used to support decisions in space and time. Further, the availability of Graphical Processing Units (GPUs) and Tensor Processing Units (TPUs) have enabled to processing of these datasets. Some of the applications of Artificial Intelligence, Machine Learning and Data Analytics are in the domains of Agriculture, Climate Change, Disaster Prediction, Automation in Manufacturing, Intelligent Transportation Systems, Health Care, Retail, Stock Market, Fashion Design, etc. The international conference on Applications of Machine Intelligence and Data Analytics aims to bring together faculty members, researchers, scientists, and industry people on a common platform to exchange ideas, algorithms, knowledge based on processing hardware and their respective application programming interfaces (APIs).

This book is a collection of selected papers presented at the Second Congress on Intelligent Systems (CIS 2021), organized by Soft Computing Research Society and CHRIST (Deemed to be University), Bengaluru, India during September 4 – 5, 2021. It includes novel and innovative work from experts, practitioners, scientists and decision-makers from academia and industry. It covers topics such as Internet of Things, information security, embedded systems, real-time systems, cloud computing, big data analysis, quantum computing, automation systems, bio-inspired intelligence, cognitive systems, cyber physical systems, data analytics, data/web mining, data science, intelligence for security, intelligent decision making systems, intelligent information processing, intelligent transportation, artificial intelligence for machine vision, imaging sensors technology, image segmentation, convolutional neural network, image/video classification, soft computing for machine vision, pattern recognition, human computer interaction, robotic devices and systems, autonomous vehicles, intelligent control systems, human motor control, game playing, evolutionary algorithms, swarm optimization, neural network, deep learning, supervised learning, unsupervised learning, fuzzy logic, rough sets, computational optimization, and neuro fuzzy systems.

This book highlights recent research on intelligent systems design and applications. It presents 100 selected papers from the 17th International Conference on Intelligent Systems Design and Applications (ISDA 2017), which was held in Delhi, India from December 14 to 16, 2017. The ISDA is a premier conference in the field of Computational Intelligence and brings together researchers, engineers and practitioners whose work involves intelligent systems and their applications in industry and the real world. Including contributions by authors from over 30 countries, the book offers a valuable reference guide for all researchers, students and practitioners in the fields of Computer Science and Engineering.

This book is a collection of best selected research papers presented at the International Conference on Communication and Artificial Intelligence (ICCAI 2020), held in the Department of Electronics & Communication Engineering, GLA University, Mathura, India, during 17–18 September 2020. The primary focus of the book is on the research information related to artificial intelligence, networks, and smart systems applied in the areas of industries, government sectors, and educational institutions worldwide. Diverse themes with a central idea of sustainable networking solutions are discussed in the book. The book presents innovative work by leading academics, researchers, and experts from industry.

A unified methodology for categorizing various complex objects is presented in this book. Through probability theory, novel asymptotically minimax criteria suitable for practical applications in imaging and data analysis are examined including the special cases such as the Jensen-Shannon divergence and the probabilistic neural network. An optimal approximate nearest neighbor search algorithm, which allows faster classification of databases is featured. Rough set theory, sequential analysis and granular computing are used to improve performance of the hierarchical classifiers. Practical examples in face identification (including deep neural networks), isolated commands recognition in voice control system and classification of visemes captured by the Kinect depth camera are included. This approach creates fast and accurate search procedures by using exact probability densities of applied dissimilarity measures. This book can be used as a guide for independent study and as supplementary material for a technically oriented graduate course in intelligent systems and data mining. Students and researchers interested in the theoretical and practical aspects of intelligent classification systems will find answers to: - Why conventional implementation of the naive Bayesian approach does not work well in image classification? - How to deal with insufficient performance of hierarchical classification systems? - Is it possible to prevent an exhaustive search of the nearest neighbor in a database?