Bøger i Atlantis Computational Intelligence Systems serien

Answer set programming (ASP) is a declarative language tailored towards solving combinatorial optimization problems. An important question when modeling continuous optimization problems is how we should handle overconstrained problems, i.e.

Featuring innovative contributions to the field such as a new bilattice-based model for trust and distrust, this book on a hot research topic is the first in-depth study of the potential of distrust in the emerging domain of trust-enhanced recommendation.

Industrial engineering is a branch of engineering dealing with the optimization of complex processes or systems. Computational Intelligence Systems find a wide application area in industrial engineering: neural networks in forecasting, fuzzy sets in capital budgeting, ant colony optimization in scheduling, Simulated Annealing in optimization, etc.

Industrial engineering is a branch of engineering dealing with the optimization of complex processes or systems. Computational Intelligence Systems find a wide application area in industrial engineering: neural networks in forecasting, fuzzy sets in capital budgeting, ant colony optimization in scheduling, Simulated Annealing in optimization, etc.

Much work on fuzzy control, covering research, development and applications, has been developed in Europe since the 90's. This book compiles the developments of researchers with demonstrated experience in the field of fuzzy control following a logic structure and a unified the style.

Over the past years, the appropriateness of Computational Intelligence (CI) techniques in modeling and optimization tasks pertaining to complex nonlinear dynamic systems has become indubitable, as attested by a large number of studies reporting on the successful application of CI models in nonlinear science (for example, adaptive control, signal processing, medical diagnostic, pattern formation, living systems, etc.). This volume summarizes the state-of-the-art of CI in the context of nonlinear dynamic systems and synchronization. Aiming at fostering new breakthroughs, the chapters in the book focus on theoretical, experimental and computational aspects of recent advances in nonlinear science intertwined with computational intelligence techniques. In addition, all the chapters have a tutorial-oriented structure.

Disaster management is a process or strategy that is implemented when any type of catastrophic event takes place. Existing studies and approaches within disaster management have mainly been focused on some specific type of disasters with certain agency oriented.

In recent years, there has been a growing interest in the need for designing intelligent systems to address complex decision systems. One of the most challenging issues for the intelligent system is to effectively handle real-world uncertainties that cannot be eliminated. These uncertainties include various types of information that are incomplete, imprecise, fragmentary, not fully reliable, vague, contradictory, deficient, and overloading. The uncertainties result in a lack of the full and precise knowledge of the decision system, including the determining and selection of evaluation criteria, alternatives, weights, assignment scores, and the final integrated decision result. Computational intelligent techniques (including fuzzy logic, neural networks, and genetic algorithms etc.), which are complimentary to the existing traditional techniques, have shown great potential to solve these demanding, real-world decision problems that exist in uncertain and unpredictable environments. These technologies have formed the foundation for intelligent systems.

Disaster management is a process or strategy that is implemented when any type of catastrophic event takes place. Existing studies and approaches within disaster management have mainly been focused on some specific type of disasters with certain agency oriented.