Engineering applications of soft computing

• Main Authors: Díaz Cortés, Margarita Arimatea (Author), Cuevas, Erik (Author), Rojas, Raúl, 1955- (Author)
• Format: Electronic eBook
• Language: English
• Published: Cham, Switzerland : Springer, 2017.
• Series: Intelligent systems reference library ; v. 129.
• Subjects: Soft computing.; elektronické knihy; electronic books
• ISBN: 9783319578132; 9783319578125
• ISSN: 1868-4394 ;
• Physical Description: 1 online resource (xv, 258 pages) : illustrations

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. Ther...

Table of Contents:

• Preface; Contents; 1 Introduction; 1.1 Soft Computing; 1.2 Fuzzy Logic; 1.3 Neural Networks; 1.4 Evolutionary Computation; 1.5 Definition of an Optimization Problem; 1.6 Classical Optimization; 1.7 Optimization with Evolutionary Computation; 1.8 Soft Computing in Engineering; References; 2 Motion Estimation Algorithm Using Block-Matching and Harmony Search Optimization; 2.1 Introduction; 2.2 Harmony Search Algorithm; 2.2.1 The Harmony Search Algorithm; 2.2.1.1 Initializing the Problem and Algorithm Parameters; 2.2.1.2 Harmony Memory Initialization; 2.2.1.3 Improvisation of New Harmony Vectors.
• 2.2.1.4 Updating the Harmony Memory2.2.2 Computational Procedure; 2.3 Fitness Approximation Method; 2.3.1 Updating the Individual Database; 2.3.2 Fitness Calculation Strategy; 2.3.3 HS Optimization Method; 2.4 Motion Estimation and Block-Matching; 2.5 Block-Matching Algorithm Based on Harmony Search with the Estimation Strategy; 2.5.1 Initial Population; 2.5.2 Tuning of the HS Algorithm; 2.5.3 The HS-BM Algorithm; 2.5.4 Discussion on the Accuracy of the Fitness Approximation Strategy; 2.6 Experimental Results; 2.6.1 HS-BM Results; 2.6.2 Results on H.264; 2.7 Conclusions; References.
• 3 Gravitational Search Algorithm Applied to Parameter Identification for Induction Motors3.1 Introduction; 3.2 Problem Statement; 3.3 Gravitational Search Algorithm; 3.4 Experimental Results; 3.4.1 Induction Motor Parameter Identification; 3.4.2 Statistical Analysis; 3.5 Conclusions; References; 4 Color Segmentation Using LVQ Neural Networks; 4.1 Introduction; 4.1.1 Histogram Thresholding and Color Space Clustering; 4.1.2 Edge Detection; 4.1.3 Probabilistic Methods; 4.1.4 Soft-Computing Techniques; 4.1.5 Scheme; 4.2 Background Issues; 4.2.1 RGB Space Color; 4.2.2 Artificial Neural Networks.
• 4.3 Competitive Networks4.4 Learning Vectors Quantization Vectors; 4.5 Architecture of the Color Segmentation System; 4.6 Implementation; 4.7 Results and Discussion; 4.8 Conclusions; References; 5 Global Optimization Using Opposition-Based Electromagnetism-Like Algorithm; 5.1 Introduction; 5.2 Electromagnetism: Like Optimization Algorithm (EMO); 5.2.1 Initialization; 5.2.2 Local Search; 5.2.3 Total Force Vector Computation; 5.2.4 Movement; 5.3 Opposition-Based Learning (OBL); 5.3.1 Opposite Number; 5.3.2 Opposite Point; 5.3.3 Opposite-Based Optimization.
• 5.4 Opposition-Based Electromagnetism-Like Optimization Algorithm5.4.1 Opposition-Based Population Initialization; 5.4.2 Opposition-Based Production for New Generation; 5.5 Experimental Results; 5.5.1 Test Problems; 5.5.2 Parameter Settings for the Involved EMO Algorithms; 5.5.3 Results; 5.6 Conclusions; References; 6 Multi-threshold Segmentation Using Learning Automata; 6.1 Introduction; 6.2 Gaussian Approximation; 6.3 Learning Automata (LA); 6.3.1 CARLA Algorithm; 6.4 Implementation; 6.5 Experimental Results; 6.5.1 LA Algorithm Performance in Image Segmentation.