Artificial Neural Network Modelling 【2016】

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"Artificial Neural Network Modelling" English | ISBN: 3319284932 | 2016 | 482 pages | PDF | 14 MB This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling.
about this series The series"Studies in Computational Intelligence"(SCI) publishes new develop ments and advances in the various areas of computational intelligence-quickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering computer science, physics and life sciences, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. Of particular value to both the contributors and the readership are the short publication timeframe and the worldwide distribution, which enable both wide and rapid dissemination of research output Moreinformationaboutthisseriesathttp:/www.springer.com/series/7092 Subana shanmuganathan Sandhya Samarasinghe Editors Artificial neural network M devlin 空 Springer editors Subana shanmuganathan Sandhya samarasinghe School of Computer and mathematical Department of Informatics and Enabling Sciences Technologies Auckland University of Technology Lincoln University Auckland Christchurch New zealand New zealand ISSN1860-949X issn 1860-9503 (electronic) tudies in Computational Intelligence ISBN978-3-319-28493-4 ISBN978-3-319-28495-8( eBook) DOI10.10071978-3-31928495-8 Library of Congress Control Number: 2015960415 o Springer International Publishing Switzerland 2016 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made Printed on acid-free paper This springer imprint is published by springernature The registered company is Springer International Publishing AG Switzerland Contents Artificial Neural Network Modelling: An Introduction Subana shanmuganathan Order in the black Box: Consistency and robustness of hidden Neuron activation of feed Forward neural Networks and Its Use in Efficient Optimization of Network Structure 15 Sandhya samarasinghe Artificial Neural networks as models of robustness in Development and regeneration: Stability of Memory During morphological remodeling. 45 Jennifer hammelman. Daniel Lobo and michael Levin a Structure Optimization algorithm of Neural Networks for Pattern Learning from Educational Data Jie Y ang, Jun Ma and Sarah K. Howard Stochastic Neural Networks for Modelling Random Processes from observed data Hong ling, Sandhya Samarasinghe and Don Kulasi 83 Curvelet Interaction with Artificial Neural Networks 109 Bharat bhosale Hybrid Wavelet Neural Network approach 127 Muhammad shoaib. Asaad Y. shamseldin Bruce W. melville and mudasser muneer Khan Quantification of Prediction Uncertainty in Artificial Neural Network models 145 K.S. Kasiviswanathan. K.P. Sudheer and jianxun he Contents Classifying Calpain Inhibitors for the Treatment of Cataracts: A Self Organising Map (SOM) ANN/KM Approach in Drug Discovery 16l I.L. Hudson. S.Y. Leemagz. A.t. Neffe and A d. abell Improved Ultrasound Based Computer Aided Diagnosis System for Breast Cancer Incorporating a New Feature of Mass Central Regularity Degree(CRD) 213 Ali al-Yousef and Sandhya samarasinghe SOM Clustering and Modelling of Australian Railway Drivers'Sleep, Wake, Duty profiles 235 Irene L. Hudson, Shalem Y. Leemaqz, Susan w. Kim, David Darwent Greg roach and drew dawson A Neural Approach to Electricity Demand Forecasting 281 Omid Motlagh, George Grozev and Elpiniki I. Papageorgiou Development of Artificial Intelligence Based Regional Flood Estimation Techniques for Eastern Australia 307 Kashif aziz. ataur rahman and asaad shamseldin Artificial Neural Networks in Precipitation Nowcasting: An Australian Case Study 325 Benjamin j.E. Schroeter Construction of PMx Concentration Surfaces Using Neural Evolutionary Fuzzy Models of Type Semi Physical Class .341 Alejandro Pena and Jesus Antonio Hernandez Application of Artificial Neural Network in Social Media Data Analysis: A Case of Lodging business in Philadelphia 369 Thai le, Phillip Pardo and william Claster Sentiment Analysis on Morphologically Rich Languages: An Artificial Neural Network(ANN) Approach 377 Nishantha Medagoda Predicting Stock Price Movements with News Sentiment: An Artificial Neural Network approach Kin-Yip Ho and wanbin (Walter)Wang Modelling Mode Choice of Individual in Linked Trips with Artificial Neural Networks and Fuzzy Representation 405 Nagesh Shukla, Jun Ma, Rohan Wickramasuriya, Nam Huynh and pascal perez Contents Artificial Neural Network (ANN Pricing Model for Natural Rubber products Based on Climate Dependencies 423 Reza setiawan, Arief Rufiyanto, Sardjono Trihatmo Budi sulistya, Erik Madyo Putro and Subana Shanmuganathan A Hybrid Artificial Neural Network(ANN) Approach to Spatial and Non-spatial Attribute Data Mining: A Case Study Experience 443 Subana shanmuganathan Artificial Neural Network Modelling An introduction Subana shanmuganathan Abstract while scientists from different disciplines, such as neuroscience, medi- cine and high performance computing, eagerly attempt to understand how the human brain functioning happens, Knowledge Engineers in computing have been successful in making use of the brain models thus far discovered to introduce heuristics into computational algorithmic modelling. Gaining further understanding on human brain/nerve cell anatomy, structure, and how the human brain functions is described to be significant especially to devise treatments for presently described as incurable brain and nervous system related diseases such as Alzheimer's and epilepsy. Despite some major breakthroughs seen over the last few decades neu roanatomists and neurobiologists of the medical world are yet to understand how we humans think, learn and remember and how our cognition and behaviour are linked. In this context, the chapter outlines the most recent human brain research initiatives following which early Artificial Neural Network (ANN) architectures, components, related terms and hybrids are elaborated 1 Introduction Neuroanatomists and neurobiologists of the medical world are yet to discover the exact structure and the real processing that takes place in human nerve cells and to biologically model the human brain. This is despite the breakthroughs made by research ever since the human beings themselves began wondering how their own thinking ability happens. More recently, there has been some major initiatives with unprecedented funding, that emphasise the drive, to accelerate research into unlocking the mysteries of human brains unique functioning. One among such big funding projects is the Human Brain Project(HBP)initiated in 2013. The HBP is a European Commission Future and Emerging Technologies Flagship that aims to S. Sha nathan(区< Auckland University of Technology, Auckland, New Zealand e-mail: Subana gmail com o Springer International Publishing Switzerland 2016 S. Shanmuganathan and S. Samarasinghe(eds ) Artificial Neural Network Modelling Studies in Computational Intelligence 628 DOI10.1007978-3-319-28495-81 S Shanmuganathan understand what makes the brain unique, the basic mechanisms behind cognition and behaviour, how to objectively diagnose brain diseases, and to build new technologies inspired by how the brain computes. There are 13 subprojects (SPs) within this ten-year one-billion pound HBP programme. The scientists involved in the hBP accept that the current computer technology is insufficient to simulate complex brain functioning. However, they are hopeful of having suffi ciently powerful supercomputers to begin the first draft simulation of the human brain within a decade. It is surprising that despite the remarkable and ground breaking innovations achieved in computing leading to transformations never seen in human development, even the modern days most powerful computers still struggle to do things that humans find instinctive. Even very young babies can recognise their mothers but programming a computer to recognise a particular person is possible but very hard. [1]. Hence, SP9 scientists of HBP are working on developing"neuromorphic computers-machines "that can learn in a similar manner to how the brain functions. The other major impediment in this regard is the humongous amount of data that will be produced, which is anticipated to require massive amount of computing memory. Currently, HBP scientists of The SpiNNaker project at the University of Manchester are building a model, which will mimic 1 of brain function. Unlocking brain functioning secrets in this manner is anticipated to yield major benefits in information technology as well. The advent of neuromorphic computers and knowledge could lead to the production of computer chips with specialised cognitive skills that truly mimic those of the human brain, such as the ability to analyse crowds or decision- making on large and complex datasets. These digital brains should also allow researchers to compare healthy and diseased brains within computer models [2] Meanwhile, across the Atlantic, the unveiling of Brain Research Through Advancing Innovative Neurotechnologies -or brain in the usa by President Obama took place in 2013 [3]. This was announced to keep up with the brain research initiated in Europe The brain project was said to begin in 2014 and be carried out by both public and private-sector scientists to map the human brain. The President announced an initial $100 m investment to shed light on how the brain works and to provide insight into diseases such as Alzheimer's, Parkinsons, epi- lepsy and many more. At the White House inauguration, President Obama said There is this enormous mystery waiting to be unlocked, and the brain initiative will change that by giving scientists the tools they need to get a dynamic picture of the brain in action and to better understand how we think and learn and remember And that knowledge will be transformative. In addition, the us President as well pointed out a lack of research in this regard, As humans we can identify galaxies light years away, we can study particles smaller than the atom, but we still havent unlocked the mystery of the 3 lb of matter that sits between our ears, "[3] that introduction to contemporary research initiatives to unlock unique human brain functioning, Sect. 2 looks at the early brain models in know ledge engineering following which initial ANN models and their architectures are elab orated. In the final section some modern day ann hybrids are outlined

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