Computational Intelligence Networked Systems And Their Applications

This book constitutes the second part of the refereed proceedings of the International Conference on Life System Modeling and Simulation, LSMS 2014, and of the International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2014, held in Shanghai, China, in September 2014. The 159 revised full papers presented in the three volumes of CCIS 461-463 were carefully reviewed and selected from 572 submissions. The papers of this volume are organized in topical sections on advanced neural network theory and algorithms; advanced evolutionary computing theory and algorithms, such as particle swarm optimization, differential evolution, ant colonies, artificial life, artificial immune systems and genetic algorithm; fuzzy, neural, and fuzzy-neuro hybrids; intelligent modeling, monitoring, and control of complex nonlinear systems; intelligent modeling and simulation of climate change; communication and control for distributed networked systems.

This book focuses on computational intelligence techniques and their applications — fast-growing and promising research topics that have drawn a great deal of attention from researchers over the years. It brings together many different aspects of the current research on intelligence technologies such as neural networks, support vector machines, fuzzy logic and evolutionary computation, and covers a wide range of applications from pattern recognition and system modeling, to intelligent control problems and biomedical applications. Fundamental concepts and essential analysis of various computational techniques are presented to offer a systematic and effective tool for better treatment of different applications, and simulation and experimental results are included to illustrate the design procedure and the effectiveness of the approaches. Sample Chapter(s) Chapter 1: Maximal Margin Algorithms for Pose Estimation (658 KB) Contents:Evolutionary Computation and Its Applications:Maximal Margin Algorithms for Pose Estimation (Ying Guo and Jiaming Li)Polynomial Modeling in a Dynamic Environment Based on a Particle Swarm Optimization (Kit Yan Chan and Tharam S Dillon)Restoration of Half-toned Color-quantized Images Using Particle Swarm Optimization with Multi-wavelet Mutation (Frank H F Leung, Benny C W Yeung and Y H Chan)Fuzzy Logics and Their Applications:Hypoglycemia Detection for Insulin-dependent Diabetes Mellitus: Evolved Fuzzy Inference System Approach (S H Ling, P P San and H T Nguyen)Neural Networks and Their Applications:Study of Limit Cycle Behavior of Weights of Perceptron (C Y F Ho and B W K Ling)Artificial Neural Network Modeling with Application to Nonlinear Dynamics (Yi Zhao)Solving Eigen-problems of Matrices by Neural Networks (Yiguang Liu, Zhisheng You, Bingbing Liu and Jiliu Zhou)Automated Screw Insertion Monitoring Using Neural Networks: A Computational Intelligence Approach to Assembly in Manufacturing (Bruno Lara, Lakmal D Seneviratne and Kaspar Althoefer)Support Vector Machines and Their Applications:On the Applications of Heart Disease Risk Classification and Hand-written Character Recognition Using Support Vector Machines (S R Alty, H K Lam and J Prada)Nonlinear Modeling Using Support Vector Machine for Heart Rate Response to Exercise (Weidong Chen, Steven W Su, Yi Zhang, Ying Guo, Nghir Nguyen, Branko G Celler and Hung T Nguyen)Machine Learning-based Nonlinear Model Predictive Control for Heart Rate Response to Exercise (Yi Zhang, Steven W Su, Branko G Celler and Hung T Nguyen)Intelligent Fault Detection and Isolation of HVAC System Based on Online Support Vector Machine (Davood Dehestani, Ying Guo, Sai Ho Ling, Steven W Su and Hung T Nguyen) Readership: Graduates and researchers in computer science, especially those specialising in artificial intelligence, neural networks, fuzzy logic and pattern recognition. Keywords:Evolutionary Computation;Fuzzy Logic;Neural Networks;Support Vector MachineKey Features:Covers wide-ranging applications from pattern recognition, control systems to biomedical applications. Various computational techniques are proposed and presented in detail for the treatment of various problemsMost of the applications in this book are real and high impact, such as hypoglycaemia, detection for diabetes patients, cardio respiratory response estimation, pattern recognition and pose estimationAddresses important related problems and difficulties using the collective experiences and knowledge from the contributors, who are each prominent in their own area of research

This book focuses on computational intelligence techniques and their applications -- fast-growing and promising research topics that have drawn a great deal of attention from researchers over the years. It brings together many different aspects of the current research on intelligence technologies such as neural networks, support vector machines, fuzzy logic and evolutionary computation, and covers a wide range of applications from pattern recognition and system modeling, to intelligent control problems and biomedical applications. Fundamental concepts and essential analysis of various computational techniques are presented to offer a systematic and effective tool for better treatment of different applications, and simulation and experimental results are included to illustrate the design procedure and the effectiveness of the approaches.

This book offers an excellent presentation of intelligent engineering and informatics foundations for researchers in this field as well as many examples with industrial application. It contains extended versions of selected papers presented at the inaugural ACASE 2012 Conference dedicated to the Applications of Systems Engineering. This conference was held from the 6th to the 8th of February 2012, at the University of Technology, Sydney, Australia, organized by the University of Technology, Sydney (Australia), Wroclaw University of Technology (Poland) and the University of Applied Sciences in Hagenberg (Austria). The book is organized into three main parts. Part I contains papers devoted to the heuristic approaches that are applicable in situations where the problem cannot be solved by exact methods, due to various characteristics or dimensionality problems. Part II covers essential issues of the network management, presents intelligent models of the next generation of networks and distributed systems as well as discusses applications of modern numerical methods in large intractable systems. Part III covers salient issues of complexity in intelligent system applications. This part also contains papers and articles which discuss concurrency issues that arise when multiple systems attempt to use the same radio space and the inter-connected system applications in the field of medical simulation and training.

"Complex Intelligent Systems and Applications" presents the most up-to-date advances in complex, software intensive and intelligent systems. Each self-contained chapter is the contribution of distinguished experts in areas of research relevant to the study of complex, intelligent, and software intensive systems. These contributions focus on the resolution of complex problems from areas of networking, optimization and artificial intelligence. The book is divided into three parts focusing on complex intelligent network systems, efficient resource management in complex systems, and artificial data mining systems. Through the presentation of these diverse areas of application, the volume provides insights into the multidisciplinary nature of complex problems. Throughout the entire book, special emphasis is placed on optimization and efficiency in resource management, network interaction, and intelligent system design. This book presents the most recent interdisciplinary results in this area of research and can serve as a valuable tool for researchers interested in defining and resolving the types of complex problems that arise in networking, optimization, and artificial intelligence.

Computational intelligence (CI) lies at the interface between engineering and computer science; control engineering, where problems are solved using computer-assisted methods. Thus, it can be regarded as an indispensable basis for all artificial intelligence (AI) activities. This book collects surveys of most recent theoretical approaches focusing on fuzzy systems, neurocomputing, and nature inspired algorithms. It also presents surveys of up-to-date research and application with special focus on fuzzy systems as well as on applications in life sciences and neuronal computing.

This book presents state-of-the-art solutions to the theoretical and practical challenges stemming from the leverage of big data and its computational intelligence in supporting smart network operation, management, and optimization. In particular, the technical focus covers the comprehensive understanding of network big data, efficient collection and management of network big data, distributed and scalable online analytics for network big data, and emerging applications of network big data for computational intelligence.