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Engineering Applications of Soft Computing Softcover reprint of the original 1st ed. 2017 [Mīkstie vāki]

  • Formāts: Paperback / softback, 258 pages, height x width: 235x155 mm, weight: 4219 g, 118 Illustrations, black and white; XV, 258 p. 118 illus., 1 Paperback / softback
  • Sērija : Intelligent Systems Reference Library 129
  • Izdošanas datums: 25-Jul-2018
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 331986257X
  • ISBN-13: 9783319862576
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  • Mīkstie vāki
  • Cena: 91,53 €*
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  • Formāts: Paperback / softback, 258 pages, height x width: 235x155 mm, weight: 4219 g, 118 Illustrations, black and white; XV, 258 p. 118 illus., 1 Paperback / softback
  • Sērija : Intelligent Systems Reference Library 129
  • Izdošanas datums: 25-Jul-2018
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 331986257X
  • ISBN-13: 9783319862576
Citas grāmatas par šo tēmu:

This book bridges the gap between Soft Computing techniques and their applications to complex engineering problems. In each chapter we endeavor to explain the basic ideas behind the proposed applications in an accessible format for readers who may not possess a background in some of the fields. Therefore, engineers or practitioners who are not familiar with Soft Computing methods will appreciate that the techniques discussed go beyond simple theoretical tools, since they have been adapted to solve significant problems that commonly arise in such areas. At the same time, the book will show members of the Soft Computing community how engineering problems are now being solved and handled with the help of intelligent approaches.

Highlighting new applications and implementations of Soft Computing approaches in various engineering contexts, the book is divided into 12 chapters. Further, it has been structured so that each chapter can be read independently of the others.

Introduction.- Motion estimation algorithm using Block-matching and
Harmony Search optimization.- Gravitational Search Algorithm applied to
parameter identification for induction motors.- Color Segmentation using LVQ
Neural Net-works.- Global Optimization using Opposition-Based
Electromagnetism-like algorithm.- Multi-Threshold Segmentation using Learning
Automata.-Real-time gaze control using Neurofuzzy prediction system.- Clonal
Selection algorithm applied to Circle detection.- States of Matter Algorithm
applied to Pattern Detection.- Artificial Bee Colony algorithm applied to
Multi-threshold Segmentation.- Learning Automata applied to Planning
Control.- Fuzzy-based System for Corner Detection.