تحسين النتائح
ترتيب حسب
من
الى
الصفحة 1
الصفحة 1
Applications of computational intelligence
Computational intelligence (CI) is the theory, design, application, and development of biologically and linguistically motivated computational paradigms. Traditionally, the three main pillars of CI have been neural networks, fuzzy systems, and evolutionary computation. However, in time, many nature-inspired computing paradigms have evolved. Thus, CI is an evolving field, and, at present, in addition to the three main constituents, it encompasses computing paradigms such as ambient intelligence, artificial life, cultural learning, artificial endocrine networks, social reasoning, and artificial hormone networks. CI plays a major role in developing successful intelligent systems, including games and cognitive developmental systems.
Adaptive Business Intelligence
In the modern information era, managers must recognize the competitive opportunities represented by decision-support tools. Adaptive business intelligence systems combine prediction and optimization techniques to assist decision makers in complex, rapidly changing environments. These systems address the fundamental questions: What is likely to happen in the future? And what is the best decision right now? Adaptive Business Intelligence includes elements of data mining, predictive modeling, forecasting, optimization, and adaptability.
LMI Approach to Analysis and Control of Takagi-Sugeno Fuzzy Systems with Time Delay
A fuzzy system is, in a very broad sense, any fuzzy logic-based system where fuzzy logic can be used either asthebasisfor the representation of different forms of system knowledge or the model for the interactions and relationships among the system variables. Fuzzy systems have proven to be an important tool for modeling complex systems for which, due to complexity or imprecision, classical tools are unsuccessful. There have been diverse fields of applications of fuzzy technology from medicine to management, from engineering to behavioral science, from vehicle control to computational linguistics, and so on. Fuzzy modeling is a conjunction to understand the s- tem’s behavior and build useful mathematical models. Different types of fuzzy models have been proposed in the literature, among which the Takagi-Sugeno (T-S) fuzzy model is a rule-based one suitable for the accurate approximation and identi?cation of a wide class of nonlinear systems.
Complexity Management in Fuzzy Systems : A Rule Base Compression Approach
This book presents a systematic study on the inherent complexity in fuzzy systems, resulting from the large number and the poor transparency of the fuzzy rules. The study uses a novel approach for complexity management, aimed at compressing the fuzzy rule base by removing the redundancy while preserving the solution. The compression is based on formal methods for presentation, manipulation, transformation and simplification of fuzzy rule bases, which are illustrated by algorithms as well as results from numerous examples and two case studies. The results are directly applicable or easily extendable to a wide class of fuzzy systems and detailed benchmarks for expanding these systems to new areas such as fuzzy networks and fuzzy multi-agent systems are introduced. The intended readers are people from both academia and industry, who would be interested in building and implementing advanced fuzzy systems.
Communications and Discoveries from Multidisciplinary Data
In this book, we aim at urging the development of data-based methods and methodologies for interdisciplinary and creative communications for solving emerging social problems. The reader shall view the direction to combine three methodological frameworks: data mining, data sharing, and communication in the contexts of sciences and businesses.
Challenges for Computational Intelligence
Computational Intelligence (CI) is used as a name to cover many existing branches of science, with artificial neural networks, fuzzy systems and evolutionary computation forming its core. In recent years CI has been extended by adding many other subdisciplines and it became quite obvious that this new field also requires a series of challenging problems that will give it a sense of direction. Without setting up clear goals and yardsticks to measure progress on the way many research efforts are wasted.The book written by top experts in CI provides such clear directions and the much-needed focus on the most important and challenging research issues, showing a roadmap how to achieve ambitious goals.
Advances in Evolutionary Computing for System Design
Evolutionary computing paradigms offer robust and powerful adaptive search mechanisms for system design. This book includes thirteen chapters covering a wide area of topics in evolutionary computing and applications including: -Introduction to evolutionary computing in system design - Evolutionary neuro-fuzzy systems - Evolution of fuzzy controllers - Genetic algorithms for multi-classifier design -Evolutionary grooming of traffic -Evolutionary particle swarms -Fuzzy logic systems using genetic algorithms - Evolutionary algorithms and immune learning for neural network-based controller design - Distributed problem solving using evolutionary learning -Evolutionary computing within grid environment -Evolutionary game theory in wireless mesh networks - Hybrid multiobjective evolutionary algorithms for the sailor assignment problem - Evolutionary techniques in hardware optimization
Advances in Dynamic Games: Applications to Economics, Finance, Optimization, and Stochastic Control
This book focuses on various aspects of dynamic game theory, presenting state-of-the-art research and serving as a guide to the vitality and growth of the field and its applications. The selected chapters, written by experts in their respective disciplines, are an outgrowth of presentations originally given at the 9th International Symposium of Dynamic Games and Applications. Featured throughout are useful tools for researchers and practitioners who use game theory for modeling in many disciplines.
Advanced Fuzzy Logic Technologies in Industrial Applications
Addresses the problem by introducing a dynamic, on-line fuzzy inference system. In this system membership functions and control rules are not determined until the system is applied and each output of its lookup table is calculated based on current inputs. The tuning process is a major focus in this volume because it is the most difficult stage in fuzzy control application. Using new methods such as µ-law technique, histogram equalization and the Bezier-based method, all detailed here, the tuning process can be significantly simplified and control performance improved. The other great strength of this book lies in the range and contemporaneity of its applications and examples which include: laser tracking and control; robot calibration; image processing and pattern recognition; medical engineering; audio systems; autonomous underwater vehicles and data mining.
