Neural Networks in a Softcomputing Framework [electronic resource] / by K. -L. Du, M. N. S. Swamy.

By: Du, K. -L [author.]Contributor(s): Swamy, M. N. S [author.] | SpringerLink (Online service)Material type: TextTextLanguage: English Publisher: London : Springer London, 2006Description: L, 566 p. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9781846283031Subject(s): Engineering | Computer science | Artificial intelligence | Optical pattern recognition | Physics | Engineering | Numerical and Computational Methods in Engineering | Complexity | Computation by Abstract Devices | Artificial Intelligence (incl. Robotics) | Signal, Image and Speech Processing | Pattern RecognitionAdditional physical formats: Printed edition:: No titleDDC classification: 006.3 LOC classification: Q342Online resources: Click here to access online
Contents:
Fundamentals of Machine Learning and Softcomputing -- Multilayer Perceptrons -- Hopfield Networks and Boltzmann Machines -- Competitive Learning and Clustering -- Radial Basis Function Networks -- Principal Component Analysis Networks -- Fuzzy Logic and Neurofuzzy Systems -- Evolutionary Algorithms and Evolving Neural Networks -- Discussion and Outlook.
In: Springer eBooksSummary: Conventional model-based data processing methods are computationally expensive and require experts’ knowledge for the modelling of a system; neural networks provide a model-free, adaptive, parallel-processing solution. Neural Networks in a Softcomputing Framework presents a thorough review of the most popular neural-network methods and their associated techniques. This concise but comprehensive textbook provides a powerful and universal paradigm for information processing. Each chapter provides state-of-the-art descriptions of the important major research results of the respective neural-network methods. A range of relevant computational intelligence topics, such as fuzzy logic and evolutionary algorithms, are introduced. These are powerful tools for neural-network learning. Array signal processing problems are discussed in order to illustrate the applications of each neural-network model. Neural Networks in a Softcomputing Framework is an ideal textbook for graduate students and researchers in this field because in addition to grasping the fundamentals, they can discover the most recent advances in each of the popular models. The systematic survey of each neural-network model and the exhaustive list of references will enable researchers and students to find suitable topics for future research. The important algorithms outlined also make this textbook a valuable reference for scientists and practitioners working in pattern recognition, signal processing, speech and image processing, data analysis and artificial intelligence.
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Fundamentals of Machine Learning and Softcomputing -- Multilayer Perceptrons -- Hopfield Networks and Boltzmann Machines -- Competitive Learning and Clustering -- Radial Basis Function Networks -- Principal Component Analysis Networks -- Fuzzy Logic and Neurofuzzy Systems -- Evolutionary Algorithms and Evolving Neural Networks -- Discussion and Outlook.

Conventional model-based data processing methods are computationally expensive and require experts’ knowledge for the modelling of a system; neural networks provide a model-free, adaptive, parallel-processing solution. Neural Networks in a Softcomputing Framework presents a thorough review of the most popular neural-network methods and their associated techniques. This concise but comprehensive textbook provides a powerful and universal paradigm for information processing. Each chapter provides state-of-the-art descriptions of the important major research results of the respective neural-network methods. A range of relevant computational intelligence topics, such as fuzzy logic and evolutionary algorithms, are introduced. These are powerful tools for neural-network learning. Array signal processing problems are discussed in order to illustrate the applications of each neural-network model. Neural Networks in a Softcomputing Framework is an ideal textbook for graduate students and researchers in this field because in addition to grasping the fundamentals, they can discover the most recent advances in each of the popular models. The systematic survey of each neural-network model and the exhaustive list of references will enable researchers and students to find suitable topics for future research. The important algorithms outlined also make this textbook a valuable reference for scientists and practitioners working in pattern recognition, signal processing, speech and image processing, data analysis and artificial intelligence.

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