Refine Results
Sort By
From
To
Page 3
Page 3
Classification and Clustering for Knowledge Discovery
This book covers recent advances in unsupervised and supervised data analysis methods in Computational Intelligence for knowledge discovery. In its first part the book provides a collection of recent research on distributed clustering, self organizing maps and their recent extensions. If labeled data or data with known associations are available, we may be able to use supervised data analysis methods, such as classifying neural networks, fuzzy rule-based classifiers, and decision trees. Therefore this book presents a collection of important methods of supervised data analysis. "Classification and Clustering for Knowledge Discovery" also includes variety of applications of knowledge discovery in health, safety, commerce, mechatronics, sensor networks, and telecommunications.
Chance Discoveries in Real World Decision Making : Data-based Interaction of Human intelligence and Artificial Intelligence
For this book, the editors invited and called for contributions from indispensable research areas relevant to "chance discovery," which has been defined as the discovery of events significant for making a decision, and studied since 2000. From respective research areas as artificial intelligence, mathematics, cognitive science, medical science, risk management, methodologies for design and communication, the invited and selected authors in this book present their particular approaches to chance discovery. The chapters here show contributions to identifying rare or hidden events and explaining their significance, predicting future trends, communications for scenario development in marketing and design, identification effects and side-effects of medicines, etc.
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.
Brain dynamics : An introduction to models and simualtions
Brain Dynamics serves to introduce graduate students and nonspecialists from various backgrounds to the field of mathematical and computational neurosciences. Some of the advanced chapters will also be of interest to the specialists. The book approaches the subject through pulse-coupled neural networks, with at their core the lighthouse and integrate-and-fire models, which allow for the highly flexible modelling of realistic synaptic activity, synchronization and spatio-temporal pattern formation. Topics also include pulse-averaged equations and their application to movement coordination. The book closes with a short analysis of models versus the real neurophysiological system.
Aspects of mathematical modelling : Applications in science, medicine, economics and management
The construction of mathematical models is an essential scientific activity. Mathematics has long been associated with developments in the exact sciences and engineering, but more recently mathematical modelling has been used to investigate complex systems that arise in many other fields. The contributors to this book demonstrate the application of mathematics to modern research topics in ecology and environmental science, health and medicine, phylogenetics and neural networks, theoretical chemistry, economics and management. The reader will find some review papers outlining current research directions in hot topics such as pattern formation and applications to medicine, and more targeted research papers on current developments in the various disciplines included.
Aspects of Automatic Text Analysis
This book It collects contributions of authors from a multidisciplinary area who focus on the topic of automatic text analysis from several (i.e. linguistic, mathematical, and information theoretical) perspectives. It describes methodological as well as methodical foundations and collects approaches in the field of text and corpus linguistics. In this sense, it contributes to the computational linguistic and information theoretical grounding of automatic text analysis.
Artificial neural networks in Vehicular Pollution Modelling
Artificial neural networks (ANNs), which are parallel computational models, comprising of interconnected adaptive processing units (neurons) have the capability to predict accurately the dispersive behavior of vehicular pollutants under complex environmental conditions. This book aims at describing step-by-step procedure for formulation and development of ANN based VP models considering meteorological and traffic parameters. The model predictions are compared with existing line source deterministic/statistical based models to establish the efficacy of the ANN technique in explaining frequent dispersion complexities in urban areas. The book is very useful for hardcore professionals and researchers working in problems associated with urban air pollution management and control.
Artificial neural networks for the Modelling and Fault Diagnosis of Technical Processes
In this book, a wide spectrum of fault diagnosis techniques have been developed. A major category of fault diagnosis techniques is the model based one, where an analytical model of the plant to be monitored is assumed to be available.
Artificial Mind System : Kernel Memory Approach
This book is written from an engineer's perspective of the mind. "Artificial Mind System" exposes the reader to a broad spectrum of interesting areas in general brain science and mind-oriented studies. In this research monograph a picture of the holistic model of an artificial mind system and its behaviour is drawn, as concretely as possible, within a unified context, which could eventually lead to practical realisation in terms of hardware or software. With a view that "the mind is a system always evolving", ideas inspired by many branches of studies related to brain science are integrated within the text, i.e. artificial intelligence, cognitive science / psychology, connectionism, consciousness studies, general neuroscience, linguistics, pattern recognition / data clustering, robotics, and signal processing.
Applied soft computing technologies : The challenge of complexity
This volume presents the proceedings of the 9th Online World Conference on Soft Computing in Industrial Applications (WSC9), September 20th - October 08th, 2004, held on the World Wide Web. It contains plenary lectures, original papers and tutorials presented during the conference. The book brings together outstanding research and developments in the field of soft computing (evolutionary computation, fuzzy logic, neural networks, and their fusion) and its applications in science and technology.
Analysis and Design of Intelligent Systems Using Soft Computing Techniques
This book comprises a selection of papers from IFSA 2007 on new methods for analysis and design of hybrid intelligent systems using soft computing techniques. Soft Computing (SC) consists of several computing paradigms, including fuzzy logic, neural networks, and genetic algorithms, which can be used to produce powerful hybrid intelligent systems for solving problems in pattern recognition, time series prediction, intelligent control, robotics and automation. Hybrid intelligent systems that combine several SC techniques are needed due to the complexity and high dimensionality of real-world problems. Hybrid intelligent systems can have different architectures, which have an impact on the efficiency and accuracy of these systems, for this reason it is very important to optimize architecture design. The architectures can combine, in different ways, neural networks, fuzzy logic and genetic algorithms, to achieve the ultimate goal of pattern recognition, time series prediction, intelligent control, or other application areas.
Advances in Mathematical and Statistical Modeling
Enrique Castillo is a leading figure in several mathematical, statistical, and engineering fields, having contributed seminal work in such areas as statistical modeling, extreme value analysis, multivariate distribution theory, Bayesian networks, neural networks, functional equations, artificial intelligence, linear algebra, optimization methods, numerical methods, reliability engineering, as well as sensitivity analysis and its applications. Organized to honor Castillo's significant contributions, this volume is an outgrowth of the International Conference on Mathematical and Statistical Modeling and covers recent advances in the field. Also presented are applications to safety, reliability and life-testing, financial modeling, quality control, general inference, as well as neural networks and computational techniques.
Advances and Innovations in Systems, Computing Sciences and Software Engineering
Acollection of world class paper articles addressing the following topics: Image and Pattern Recognition: Compression, Image processing, Signal Processing Architectures, Signal Processing for Communication, Signal Processing Implementation, Speech Compression, and Video Coding Architectures. Languages and Systems: Algorithms, Databases, Embedded Systems and Applications, File Systems and I/O, Geographical Information Systems, Kernel and OS Structures, Knowledge Based Systems, Modeling and Simulation, Object Based Software Engineering, Programming Languages, and Programming Models and tools. Parallel Processing: Distributed Scheduling, Multiprocessing, Real-time Systems, Simulation Modeling and Development, and Web Applications. New trends in computing: Computers for People of Special Needs, Fuzzy Inference, Human Computer Interaction, Incremental Learning, Internet-based Computing Models, Machine Intelligence, Natural Language Processing, Neural Networks, and Online Decision Support System.
Advanced Computational Intelligence Paradigms in Healthcare - 3
Advanced Computational Intelligence (CI) paradigms are increasingly used for implementing robust computer applications to foster safety, quality and efficacy in all aspects of healthcare. This research book covers an ample spectrum of the most advanced applications of CI in healthcare.
Absolute Stability of Nonlinear Control Systems
Following the recent developments in the field of absolute stability, Professor Xiaoxin Liao, in conjunction with Professor Pei Yu, has created a second edition of his seminal work on the subject. Liao begins with an introduction to the Lurie problem and the Lurie control system, before moving on to the simple algebraic sufficient conditions for the absolute stability of autonomous and non-autonomous ODE systems, as well as several special classes of Lurie-type systems. The focus of the book then shifts toward the new results and research that have appeared in the decade since the first edition was published. This includes nonlinear control systems with multiple controls, interval control systems, time-delay and neutral Lurie control systems, systems described by functional differential equations, the absolute stability for neural networks, as well as applications to chaos control and chaos synchronization.
