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Complex Systems in Knowledge-based Environments: Theory, Models and Applications
The tremendous growth in the availability of inexpensive computing power and easy availability of computers have generated tremendous interest in the design and imp- mentation of Complex Systems. Computer-based solutions offer great support in the design of Complex Systems. Furthermore, Complex Systems are becoming incre- ingly complex themselves. This research book comprises a selection of state-of-the-art contributions to topics dealing with Complex Systems in a Knowledge-based En- ronment. Complex systems are ubiquitous. Examples comprise, but are not limited to System of Systems, Service-oriented Approaches, Agent-based Systems, and Complex Distributed Virtual Systems. These are applicat...
Innovations in Swarm Intelligence
Over the past two decades, swarm intelligence has emerged as a powerful approach to solving optimization as well as other complex problems. Swarm intelligence models are inspired by social behaviours of simple agents interacting among themselves as well as with the environment, e.g., flocking of birds, schooling of fish, foraging of bees and ants. The collective behaviours that emerge out of the interactions at the colony level are useful in achieving complex goals. The main aim of this research book is to present a sample of recent innovations and advances in techniques and applications of swarm intelligence. Among the topics covered in this book include: particle swarm optimization and hyb...
Applications of Soft Computing
Soft Computing is a complex of methodologies that includes artificial neural networks, genetic algorithms, fuzzy logic, Bayesian networks, and their hybrids. It admits approximate reasoning, imprecision, uncertainty and partial truth in order to mimic the remarkable human capability of making decisions in real-life, ambiguous environments. Soft Computing has therefore become popular in developing systems that encapsulate human expertise. 'Applications of Soft Computing: Updating the State of Art' contains a collection of papers that were presented at the 12th On-line World Conference on Soft Computing in Industrial Applications, held in October 2007. This carefully edited book provides a com...
Biologically Inspired Signal Processing for Chemical Sensing
Biologically inspired approaches for artificial sensing have been extensively applied to different sensory modalities over the last decades and chemical senses have been no exception. The olfactory system, and the gustatory system to a minor extent, has been regarded as a model for the development of new artificial chemical sensing s- tems. One of the main contributions to this field was done by Persaud and Dodd in 1982 when they proposed a system based on an array of broad-selective chemical sensors coupled with a pattern recognition engine. The array aimed at mimicking the sensing strategy followed by the olfactory system where a population of bro- selective olfactory receptor neurons enco...
Advances of Computational Intelligence in Industrial Systems
Computational Intelligence (CI) has emerged as a rapidly growing field over the past decade. This volume reports the exploration of CI frontiers with an emphasis on a broad spectrum of real-world applications. Such a collection of chapters has presented the state-of-the-art of CI applications in industry and will be an essential resource for professionals and researchers who wish to learn and spot the opportunities in applying CI techniques to their particular problems.
Aspects of Soft Computing, Intelligent Robotics and Control
Soft computing, as a collection of techniques exploiting approximation and tolerance for imprecision and uncertainty in traditionally intractable problems, has become very effective and popular especially because of the synergy derived from its components. The integration of constituent technologies provides complementary methods that allow developing flexible computing tools and solving complex problems. A wide area of natural applications of soft computing techniques consists of the control of dynamic systems, including robots. Loosely speaking, control can be understood as driving a process to attain a desired goal. Intelligent control can be seen as an extension of this concept, to inclu...
New Challenges in Computational Collective Intelligence
Collective intelligence has become one of major research issues studied by today’s and future computer science. Computational collective intelligence is understood as this form of group intellectual activity that emerges from collaboration and compe- tion of many artificial individuals. Robotics, artificial intelligence, artificial cognition and group working try to create efficient models for collective intelligence in which it emerges from sets of actions carried out by more or less intelligent individuals. The major methodological, theoretical and practical aspects underlying computational collective intelligence are group decision making, collective action coordination, collective comp...
Discrete-Time High Order Neural Control
Neural networks have become a well-established methodology as exempli?ed by their applications to identi?cation and control of general nonlinear and complex systems; the use of high order neural networks for modeling and learning has recently increased. Usingneuralnetworks,controlalgorithmscanbedevelopedtoberobustto uncertainties and modeling errors. The most used NN structures are Feedf- ward networks and Recurrent networks. The latter type o?ers a better suited tool to model and control of nonlinear systems. There exist di?erent training algorithms for neural networks, which, h- ever, normally encounter some technical problems such as local minima, slow learning, and high sensitivity to in...
Advances in Data Management
Data Management is the process of planning, coordinating and controlling data resources. More often, applications need to store and search a large amount of data. Managing Data has been continuously challenged by demands from various areas and applications and has evolved in parallel with advances in hardware and computing techniques. This volume focuses on its recent advances and it is composed of five parts and a total of eighteen chapters. The first part of the book contains five contributions in the area of information retrieval and Web intelligence: a novel approach to solving index selection problem, integrated retrieval from Web of documents and data, bipolarity in database querying, deriving data summarization through ontologies, and granular computing for Web intelligence. The second part of the book contains four contributions in knowledge discovery area. Its third part contains three contributions in information integration and data security area. The remaining two parts of the book contain six contributions in the area of intelligent agents and applications of data management in medical domain.
Fuzzy-Like Multiple Objective Decision Making
Decision makers usually face multiple, conflicting objectives and the complicated fuzzy-like environments in the real world. What are the fuzzy-like environments? How do we model the multiple objective decision making problems under fuzzy-like environments? How do you deal with these models? In order to answer these questions, this book provides an up-to-date methodology system for fuzzy-like multiple objective decision making, which includes modelling system, model analysis system, algorithm system and application system in structure optimization problem, selection problem, purchasing problem, inventory problem, logistics problem and so on. Researchers, practitioners and students in management science, operations research, information science, system science and engineering science will find this work a useful reference.
