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Neural Networks in a Softcomputing Framework

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Description

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.

Neural Networks in a Softcomputing Framework
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  • 77,99 €
  • Available on iPhone, iPad, iPod touch, and Mac.
  • Category: Computers
  • Published: 02 August 2006
  • Publisher: Springer London
  • Print Length: 616 Pages
  • Language: English
  • Requirements: To view this book, you must have an iOS device with iBooks 1.3.1 or later and iOS 4.3.3 or later, or a Mac with iBooks 1.0 or later and OS X 10.9 or later.

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