الصفحة 5
الصفحة 5
Evolving Connectionist Systems : The Knowledge Engineering Approach
Evolving Connectionist Systems is aimed at all those interested in developing and using intelligent computational models and systems to solve challenging real world problems in computer science, engineering, bioinformatics and neuroinformatics. The book challenges scientists and practitioners with open questions about future creation of new information models inspired by Nature. This edition includes new methods for adaptive, knowledge-based learning, such as online incremental feature selection, spiking neural networks, transductive neuro-fuzzy inference, adaptive data and model integration, cellular automata and artificial life systems, particle swarm optimisation, ensembles of evolving systems, and quantum inspired neural networks. New applications to gene and protein interaction modelling, brain data analysis and brain model creation, computational neuro-genetic modelling, adaptive speech, image and multimodal recognition, language modelling, adaptive robotics, modelling dynamic financial and socio-economic systems, and ecological modelling, are covered. An important new feature of the book is the attempt to connect different structural and functional levels of a complex, intelligent system, looking for inspiration from functional relationships in natural systems, such as the genetic and the brain activity.
Engineering Evolutionary Intelligent Systems
This edited volume 'Engineering Evolutionary Intelligent Systems' deals with the theoretical and methodological aspects, as well as various evolutionary algorithm applications to many real world problems originating from science, technology, business or commerce.
Differential Evolution Algorithm with Type-2 Fuzzy Logic for Dynamic Parameter Adaptation with Application to Intelligent Control
This book focuses on the fields of fuzzy logic, bio-inspired algorithm, especially the differential evolution algorithm and also considering the fuzzy control area. The main idea is that these two areas together can help solve various control problems and to find better results. In this book, the authors test the proposed method using five benchmark control problems. First, the water tank, temperature, mobile robot, and inverted pendulum controllers are considered. For these 4 problems, experimentation was carried out using a Type-1 fuzzy system and an Interval Type-2 system. The last control problem was the D.C. motor, for which the experiments were performed with Type-1, Interval Type-2, and Generalized Type-2 fuzzy systems. When we use fuzzy systems combined with the differential evolution algorithm, we can notice that the results obtained in each of the controllers are better and with increasing uncertainty, the results are even better. For this reason, the authors consider in this book the proposed method using fuzzy systems and the differential evolution algorithm to improve the fuzzy controllers’ behavior in complex control problems.
Control of Traffic Systems in Buildings
Control of Traffic Systems in Buildings presents the state of the art in the analysis and control of transportation systems in buildings focusing primarily on elevator groups. The theory and design of passenger traffic and cargo transport systems are covered, together with actual operational examples and topics of special current interest such as: • noisy, on-line and algorithmic optimization; • simulation-based modeling of passengers and goods; • control of cooperative agent-oriented systems; • proposal for a benchmark to compare new control methods; • deployment and testing of transportation systems.
Computational Intelligence, Theory and Applications ; International Conference 9th Fuzzy Days in Dortmund, Germany, Sept. 18-20, 2006 Proceedings
This book constitutes the refereed proceedings of the 9th Dortmund Fuzzy Days, held in Dortmund, Germany, 2006. The Fuzzy Days conference has established itself as an international forum for the discussion of new results in the field of Computational Intelligence. All the papers had to undergo a thorough review guaranteeing a solid quality of the programme. The papers are devoted to foundational and practical issues in fuzzy systems, neural networks, evolutionary algorithms, and machine learning and thus cover the whole range of computational intelligence.
Computational intelligence in time series forecasting : Theory and engineering applications
Deals with the power of intelligent technologies individually and in combination. This book includes examples of the particular systems and processes susceptible to each technique. It is suitable for industrial training purposes, as well as serving as a useful reference material for experimental researchers.
Computational Intelligence in Multimedia Processing : Recent Advances
This book presents a large number of interesting applications to intelligent multimedia processing of various Computational Intelligence techniques, such as rough sets, Neural Networks; Fuzzy Logic; Evolutionary Computing; Artificial Immune Systems; Swarm Intelligence; Reinforcement Learning and evolutionary computation.
Computational Intelligence in Fault Diagnosis
Presenting the latest developments and research results on fault diagnosis approaches using computational intelligence methodologies, this book opens with a review of the state-of-the-art before moving on to focus on various theoretical aspects of computational intelligence methodologies applied to real-world fault diagnosis problems.
Computational intelligence in economics and finance ; Vol. II
Computational intelligence (CI), as an alternative to statistical and econometric approaches, has been applied to a wide range of economics and finance problems in recent years, for example to price forecasting and market efficiency. This book contains research ranging from applications in financial markets and business administration to various economics problems. Not only are empirical studies utilizing various CI algorithms presented, but so also are theoretical models based on computational methods. In addition to direct applications of computational intelligence, readers can also observe how these methods are combined with conventional analytical methods such as statistical and econometric models to yield preferred results.
Computational intelligence in bioinformatics
This book deals with the application of computational intelligence in bioinformatics. Addressing the various issues of bioinformatics using different computational intelligence approaches is the novelty of this edited volume.
Computational intelligence for modelling and prediction
This book contains recent advances in Computational Intelligence methods for modeling, optimization and prediction and covers a large number of applications. The book presents new Computational Intelligence theory and methods for modeling and prediction. The range of the various applications is captured with 5 chapters in image processing, 2 chapters in audio processing, 3 chapters in commerce and finance, 2 chapters in communication networks and 6 chapters containing other applications.
Computational intelligence and bioinspired systems ; 8th International Work-conference on artificial neural networks, IWANN 2005, Vilanova i la Geltrú, Barcelona, Spain, June 8-10, 2005, Proceedings
We present in this volume the collection of finally accepted papers of the eighth edition of the “IWANN” conference (“International Work-Conference on Artificial Neural Networks”). This biennial meeting focuses on the foundations, theory, models and applications of systems inspired by nature (neural networks, fuzzy logic and evolutionary systems). For scientists, engineers and professionals working in the area, this is a very good way to get solid and competitive applications. We are facing a real revolution with the emergence of embedded intelligence in many artificial systems (systems covering diverse fields: industry, domotics, leisure, healthcare, … ). So we are convinced that an enormous amount of work must be, and should be, still done. Many pieces of the puzzle must be built and placed into their proper positions, offering us new and solid theories and models (necessary tools) for the application and praxis of these current paradigms. The above-mentioned concepts were the main reason for the subtitle of the IWANN 2005 edition: “Computational Intelligence and Bioinspired Systems.” papers was addressing the following topics: 1. Mathematical and theoretical methods in computational intelligence.
Logic Programming : 24th International Conference, ICLP 2008 Udine, Italy, December 9-13 2008 Proceedings
The 35 revised full papers together with 2 invited talks, 2 invited tutorials, 11 papers of the co-located first Workshop on Answer Set Programming and Other Computing Paradigms (ASPOCP 2008), as well as 26 poster presentations and the abstracts of 11 doctoral consortium articles were carefully reviewed and selected from 177 initial submissions. The papers cover all issues of current research in logic programming - they are organized in topical sections on applications, algorithms, systems, and implementations, semantics and foundations, analysis and transformations, CHRs and extensions, implementations and systems, answer set programming and extensions, as well as constraints and optimizations.
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.
Learning Classifier Systems in Data Mining
Just over thirty years after Holland first presented the outline for Learning Classifier System paradigm, the ability of LCS to solve complex real-world problems is becoming clear. In particular, their capability for rule induction in data mining has sparked renewed interest in LCS. This book brings together work by a number of individuals who are demonstrating their good performance in a variety of domains.
Knowledge Processing with Interval and Soft Computing
In particular, these chapters cover computing techniques for interval linear systems of equations, interval matrix singular-value decomposition, interval function approximation, and decision making with statistical and graph-based data processing. To enable these applications, the book presents a standards-based object-oriented interval computing environment in C++.
Computational and Ambient Intelligence ; 9th International Work-Conference on Artificial Neural Networks, IWANN 2007, San Sebastián, Spain, June 20-22, 2007, Proceedings
This biennial meeting focuses on the foundations, theory, models and applications of systems inspired by nature (neural networks, fuzzy logic and evo- tionary systems).These new computational techniques are used in applications that try to bring a new situation of well-being to the user. The conjunction of a more and more miniaturized hardware together with the growing computational intelligence embodied in this hardware leads us towards fully integrated embedded systems-on- chip and opens the door for truly ubiquitous electronics.
Classification and Modeling with Linguistic Information Granules : Advanced Approaches to Linguistic Data Mining
Many approaches have already been proposed for classification and modeling in the literature. These approaches are usually based on mathematical mod els. Computer systems can easily handle mathematical models even when they are complicated and nonlinear (e.g., neural networks). On the other hand, it is not always easy for human users to intuitively understand mathe matical models even when they are simple and linear. This is because human information processing is based mainly on linguistic knowledge while com puter systems are designed to handle symbolic and numerical information. A large part of our daily communication is based on words. We learn from various media such as books, newspapers, magazines, TV, and the Inter net through words. We also communicate with others through words. While words play a central role in human information processing, linguistic models are not often used in the fields of classification and modeling. If there is no goal other than the maximization of accuracy in classification and modeling, mathematical models may always be preferred to linguistic models. On the other hand, linguistic models may be chosen if emphasis is placed on interpretability.
Classic Works on the Dempster-Shafer Theory of Belief Functions
This book brings together a collection of classic research papers on the Dempster-Shafer theory of belief functions. By bridging fuzzy logic and probabilistic reasoning, the theory of belief functions has become a primary tool for knowledge representation and uncertainty reasoning in expert systems.
Artificial neural networks - ICANN 2008 ; 18th International Conference, Prague, Czech Republic, September 3-6, 2008, Proceedings, Part I
This two volume set LNCS 5163 and LNCS 5164 constitutes the refereed proceedings of the 18th International Conference on Artificial Neural Networks, ICANN 2008, held in Prague Czech Republic, in September 2008.
