ARTIFICIAL NEURAL NETWORKS ͳ INDUSTRIAL AND CONTROL ENGINEERING APPLICATIONS Edited by Kenji Suzuki Artificial Neural Networks - Industrial and Control Engineering Applications Edited by Kenji Suzuki Published by InTech Janeza Trdine 9, 51000 Rijeka, Croatia Copyright © 2011 InTech All chapters are Open Access articles distributed under the Creative Commons Non Commercial Share Alike Attribution 3.0 license, which permits to copy, distribute, transmit, and adapt the work in any medium, so long as the original work is properly cited. After this work has been published by InTech, authors have the right to republish it, in whole or part, in any publication of which they are the author, and to make other personal use of the work. Any republication, referencing or personal use of the work must explicitly identify the original source. Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published articles. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book. Publishing Process Manager Ivana Lorkovic Technical Editor Teodora Smiljanic Cover Designer Martina Sirotic Image Copyright 2010. Used under license from Shutterstock.com First published March, 2011 Printed in India A free online edition of this book is available at www.intechopen.com Additional hard copies can be obtained from orders@intechweb.org Artificial Neural Networks - Industrial and Control Engineering Applications, Edited by Kenji Suzuki p. cm. ISBN 978-953-307-220-3 free online editions of InTech Books and Journals can be found at www.intechopen.com Part 1 Chapter 1 Chapter 2 Chapter 3 Part 2 Chapter 4 Chapter 5 Chapter 6 Chapter 7 Preface IX Textile Industry 1 Review of Application of Artificial Neural Networks in Textiles and Clothing Industries over Last Decades 3 Chi Leung Parick Hui, Ng Sau Fun and Connie Ip Artificial Neural Network Prosperities in Textile Applications 35 Mohammad Amani Tehran and Mahboubeh Maleki Modelling of Needle-Punched Nonwoven Fabric Properties Using Artificial Neural Network 65 Sanjoy Debnath Materials Science and Industry 89 Artificial Neural Networks for Material Identification, Mineralogy and Analytical Geochemistry Based on Laser-Induced Breakdown Spectroscopy 91 Alexander Koujelev and Siu-Lung Lui Application of Artificial Neural Networks in the Estimation of Mechanical Properties of Materials 117 Seyed Hosein Sadati, Javad Alizadeh Kaklar and Rahmatollah Ghajar Optimum Design and Application of Nano-Micro-Composite Ceramic Tool and Die Materials with Improved Back Propagation Neural Network 131 Chonghai Xu, Jingjie Zhang and Mingdong Yi Application of Bayesian Neural Networks to Predict Strength and Grain Size of Hot Strip Low Carbon Steels 153 Mohammad Reza Toroghinejad and Mohsen Botlani Esfahani Contents Contents VI Adaptive Neuro-Fuzzy Inference System Prediction of Calorific Value Based on the Analysis of U.S. Coals 169 F. Rafezi, E. Jorjani and Sh. Karimi Artificial Neural Network Applied for Detecting the Saturation Level in the Magnetic Core of a Welding Transformer 183 Klemen Deželak, Gorazd Štumberger, Drago Dolinar and Beno Klopčič Food Industry 199 Application of Artificial Neural Networks to Food and Fermentation Technology 201 Madhukar Bhotmange and Pratima Shastri Application of Artificial Neural Networks in Meat Production and Technology 223 Maja Prevolnik, Dejan Škorjanc, Marjeta Čandek-Potokar and Marjana Novič Electric and Power Industry 241 State of Charge Estimation of Ni-MH battery pack by using ANN 243 Chang-Hao Piao, Wen-Li Fu, Jin-Wang, Zhi-Yu Huang and Chongdu Cho A Novel Frequency Tracking Method Based on Complex Adaptive Linear Neural Network State Vector in Power Systems 259 M. Joorabian, I. Sadinejad and M. Baghdadi Application of ANN to Real and Reactive Power Allocation Scheme 283 S.N. Khalid, M.W. Mustafa, H. Shareef and A. Khairuddin Mechanical Engineering 307 The Applications of Artificial Neural Networks to Engines 309 Deng, Jiamei, Stobart, Richard and Maass, Bastian A Comparison of Speed-Feed Fuzzy Intelligent System and ANN for Machinability Data Selection of CNC Machines 333 Zahari Taha and Sarkawt Rostam Chapter 8 Chapter 9 Part 3 Chapter 10 Chapter 11 Part 4 Chapter 12 Chapter 13 Chapter 14 Part 5 Chapter 15 Chapter 16 Contents VII Control and Robotic Engineering 357 Artificial Neural Network – Possible Approach to Nonlinear System Control 359 Jan Mareš, Petr Doležel and Pavel Hrnčiřík Direct Neural Network Control via Inverse Modelling: Application on Induction Motors 377 Haider A. F. Almurib, Ahmad A. Mat Isa and Hayder M.A.A. Al-Assadi System Identification of NN-based Model Reference Control of RUAV during Hover 395 Bhaskar Prasad Rimal, Idris E. Putro, Agus Budiyono, Dugki Min and Eunmi Choi Intelligent Vibration Signal Diagnostic System Using Artificial Neural Network 421 Chang-Ching Lin Conditioning Monitoring and Fault Diagnosis for a Servo-Pneumatic System with Artificial Neural Network Algorithms 441 Mustafa Demetgul, Sezai Taskin and Ibrahim Nur Tansel Neural Networks’ Based Inverse Kinematics Solution for Serial Robot Manipulators Passing Through Singularities 459 Ali T. Hasan, Hayder M.A.A. Al-Assadi and Ahmad Azlan Mat Isa Part 6 Chapter 17 Chapter 18 Chapter 19 Chapter 20 Chapter 21 Chapter 22 Pref ac e Artifi cial neural networks may probably be the single most successful technology in the last two decades which has been widely used in a large variety of applications in various areas. An artifi cial neural network, o en just called a neural network, is a mathematical (or computational) model that is inspired by the structure and function of biological neural networks in the brain. An artifi cial neural network consists of a number of artifi cial neurons (i.e., nonlinear processing units) which are connected each other via synaptic weights (or simply just weights). An artifi cial neural network can “learn” a task by adjusting weights. There are supervised and unsupervised models. A supervised model requires a “teacher” or desired (ideal) output to learn a task. An unsupervised model does not require a “teacher,” but it leans a task based on a cost function associated with the task. An artifi cial neural network is a powerful, versatile tool. Artifi cial neural networks have been successfully used in various applications such as biological, medical, industrial, control engendering, so ware engineering, environmental, economical, and social applications. The high versatility of artifi cial neural networks comes from its high capability and learning function. It has been theoretically proved that an artifi cial neural network can approximate any continu- ous mapping by arbitrary precision. Desired continuous mapping or a desired task is acquired in an artifi cial neural network by learning. The purpose of this book series is to provide recent advances of artifi cial neural net- work applications in a wide range of areas. The series consists of two volumes: the fi rst volume contains methodological advances and biomedical applications of artifi cial neural networks; the second volume contains artifi cial neural network applications in industrial and control engineering. This second volume begins with a part of artifi cial neural network applications in tex- tile industries which are concerned with the design and manufacture of clothing as well as the distribution and use of textiles. The part contains a review of various appli- cations of artifi cial neural networks in textile and clothing industries as well as partic- ular applications. A part of materials science and industry follows. This part contains applications of artifi cial neural networks in material identifi cation, and estimation of material property, behavior, and state. Parts continue with food industry such as meat, electric and power industry such as ba eries, power systems, and power allocation systems, mechanical engineering such as engines and machines, control and robotic engineering such as nonlinear system control, induction motors, system identifi cation, signal and fault diagnosis systems, and robot manipulation. X Preface Thus, this book will be a fundamental source of recent advances and applications of artifi cial neural networks in industrial and control engineering areas. The target audi- ence of this book includes professors, college students, and graduate students in engi- neering schools, and engineers and researchers in industries. I hope this book will be a useful source for readers and inspire them. Kenji Suzuki, Ph.D. University of Chicago Chicago, Illinois, USA [...]... criteria, and model validation 7 Potential future application of ANN in textiles and clothing industries A large number of applications of ANN in textiles and clothing industries are used feedforward and Kohonen networks The other types of artificial neural networks such as recurrent neural network, associative neural network and dynamic neural networks (refer to http://en.wikipedia.org/wiki/Types_of _artificial_ neural_ networks. .. experts 12 Artificial Neural Networks - Industrial and Control Engineering Applications 3.5 Seam performance Hui and Ng (2009) investigated the capability of artificial neural networks based on a back propagation algorithm with weight decay technique and multiple logarithm regression (MLR) methods for modeling seam performance of fifty commercial woven fabrics used for the manufacture of men’s and women’s... fabrics for suiting, shirting, and blouse uses were selected and fabric properties of extension, shear, bending, compression, and friction and 8 Artificial Neural Networks - Industrial and Control Engineering Applications roughness were measured by using the Kawabata KES instruments Instrumental data of the fabric properties and information on fabric end-uses were input into neural network software to... textiles and clothing industries will be addressed in last section 4 Artificial Neural Networks - Industrial and Control Engineering Applications 2 Applications to fibres and yarns 2.1 Fibre classification Kang and Kim (2002) developed an image system for the current cotton grading system of raw cotton involving a trained artificial neural network with a good classifying ability Trash from a raw cotton... of both neps and trash According to experimental analysis, the recognition rate can reach 99.63% under circumstances in which the neural network topology is 3-3-3 Both contrast and brightness were set at 60% with an azure background color The results showed that both neps and 10 Artificial Neural Networks - Industrial and Control Engineering Applications trash can be recognized well, and the method... yarns of 26 woven fabrics manufactured by air jet loom by using neural net model which were used to determine the relationships between the shrinkage of yarns and the cover factors of yarns and fabrics Author Journal No Title 18 Artificial Neural Networks - Industrial and Control Engineering Applications Study Area No Title 10 An Artificial Neural Network-based Hairiness Prediction Model for Worsted Wool... into account changes in materials’ specifications and processing techniques within a given mill The MLP model does possess this characteristic and has the potential for wider applicability in industry / 20 Artificial Neural Networks - Industrial and Control Engineering Applications 15 Artificial Neural Network System for the Design of Airbag Fabrics Behera and Goyal Journal of 2009 39(1), Indu45-55 strial... training pairs and 22 Artificial Neural Networks - Industrial and Control Engineering Applications Study Area Prediction performance can be further improved by including these parameters as input, during the training phase investigated the prediction of nonlinear relations of functional and aesthetic properties of worsted suiting fabrics for 19 Artificial neural Behera network-based and prediction... Textile 2002 72(9), 2.1 Fibre 1 Objective Evaluation Kang for the current 776-782 classification of the Trash and Color and Kim Research cotton grading Journal of Raw Cotton by system of raw Image Processing and cotton Neural Network 16 Artificial Neural Networks - Industrial and Control Engineering Applications 8 Appendix Study Area 2.3 Yarnproperty prediction Predicting the Tensile Zeng et Textile 2004... self-learning, and fuzzy reasoning The model was shown that these three elements can generated the best predictions compared with other hybrid models All research outputs in application of ANN in textiles and clothing areas over last decade are summarized as shown in Appendix 14 Artificial Neural Networks - Industrial and Control Engineering Applications 6 Challenges encountered by ANN used in textiles and clothing . ARTIFICIAL NEURAL NETWORKS ͳ INDUSTRIAL AND CONTROL ENGINEERING APPLICATIONS Edited by Kenji Suzuki Artificial Neural Networks - Industrial and Control Engineering Applications Edited. properties of extension, shear, bending, compression, and friction and Artificial Neural Networks - Industrial and Control Engineering Applications 8 roughness were measured by using the. 199 Application of Artificial Neural Networks to Food and Fermentation Technology 201 Madhukar Bhotmange and Pratima Shastri Application of Artificial Neural Networks in Meat Production and Technology