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"Soft computing Congresses."
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Soft Computing Systems
This volume focuses on research developments on intelligent systems in a hybrid environment and its applications in image processing, Internet modelling and data mining. The contributions presented were accepted for the Second International Conference on Hybrid Intelligent Systems (HIS '02).
eBook
Systematic organisation of information in fuzzy systems
This work presents new evolutions and progresses in the domain of information processing and organization in and by fuzzy systems and other types of systems using uncertain information. Also, information aggregation and organization by means of tools offered by fuzzy logic are dealt with.
eBook
A new paradigm of knowledge engineering by soft computing
Soft computing (SC) consists of several computing paradigms, including neural networks, fuzzy set theory, approximate reasoning, and derivative-free optimization methods such as genetic algorithms. The integration of those constituent methodologies forms the core of SC. In addition, the synergy allows SC to incorporate human knowledge effectively, deal with imprecision and uncertainty, and learn to adapt to unknown or changing environments for better performance. Together with other modern technologies, SC and its applications exert unprecedented influence on intelligent systems that mimic human intelligence in thinking, learning, reasoning, and many other aspects.Knowledge engineering (KE), which deals with knowledge acquisition, representation, validation, inferencing, explanation, and maintenance, has made significant progress recently, owing to the indefatigable efforts of researchers. Undoubtedly, the hot topics of data mining and knowledge/data discovery have injected new life into the classical AI world.This book tells readers how KE has been influenced and extended by SC and how SC will be helpful in pushing the frontier of KE further. It is intended for researchers and graduate students to use as a reference in the study of knowledge engineering and intelligent systems. The reader is expected to have a basic knowledge of fuzzy logic, neural networks, genetic algorithms, and knowledge-based systems.
eBook
Performance analysis of stirling engine using computational intelligence techniques (ANN & Fuzzy Mamdani Model) and hybrid algorithms (ANN-PSO & ANFIS)
Stirling engine is considered as one of the most promising alternatives to conventional combustion units due to its versatility and potential to achieve relatively high efficiency. The output power and torque are the main performance indicators that depend on many variables. Many studies have pointed out that the relationship between the performance indicators of the Stirling engine and its input variables was nonlinear. This study analyses the prediction performance of power and torque in a Stirling engine system using soft computing techniques—artificial neural network (ANN) and Fuzzy Mamdani Model (FMM) and hybrid algorithms—adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network trained with particle swarm optimization (ANN-PSO). The performance of these approaches has been discussed using a dataset from a test conducted on an existing Stirling engine. The performance indicators of the different models considering the power and the torque were predicted and analysed. A parametric analysis has been performed for the ANN-PSO model to identify the best model configuration considering the number of neurons in hidden layers, the number of swarm size and acceleration factors. A detailed description of the process leading to the identification of the best networks architecture for the power and torque model has been provided. The comparison of the four approaches indicates that FMM exhibits the highest performance prediction considering the power while the ANN-PSO and ANFIS model exhibit the highest performance considering the torque. This study demonstrates the suitability of soft computing techniques and hybrid algorithms for the prediction of Stirling engine characteristics and its potential to optimize time and experimental cost.
Journal Article
Cellular neural networks, multi-scroll chaos and synchronization
For engineering applications that are based on nonlinear phenomena, novel information processing systems require new methodologies and design principles. This perspective is the basis of the three cornerstones of this book: cellular neural networks, chaos and synchronization. Cellular neural networks and their universal machine implementations offer a well-established platform for processing spatial-temporal patterns and wave computing. Multi-scroll circuits are generalizations to the original Chua's circuit, leading to chip implementable circuits with increasingly complex attractors. Several applications make use of synchronization techniques for nonlinear systems. A systematic overview is given for Lur'e representable systems with global synchronization criteria for master-slave and mutual synchronization, robust synchronization, H∞ synchronization, time-delayed systems and impulsive synchronization.
eBook
Blind Equalization in Neural Networks
The book begins with an introduction of blind equalization theory and its application in neural networks, then discusses the algorithms in recurrent networks, fuzzy networks and other frequently-studied neural networks.
eBook
Connectionist models of cognition and perception II
This book collects together refereed versions of papers presented at the Eighth Neural Computation and Psychology Workshop (NCPW 8). NCPW is a well-established workshop series that brings together researchers from different disciplines, such as artificial intelligence, cognitive science, computer science, neurobiology, philosophy and psychology. The articles are centred on the theme of connectionist modelling of cognition and perceptionn.
eBook
Soft Computing Techniques for Machining of Composites
Special topic volume with invited peer reviewed papers only.
eBook
Neural networks for instrumentation, measurement and related industrial applications
This work aims to disseminate theoretical and practical knowledge about neural networks in measurement, instrumentation and the related industrial applications. It also creates a consciousness about the effectiveness of these techniques as well as the measurement problems in industrial environments.
eBook