January 25, 2021

Download Ebook Free Artificial Neural Networks For Engineering Applications

Artificial Neural Networks for Engineering Applications

Artificial Neural Networks for Engineering Applications
Author : Alma Y. Alanis,Nancy Arana-Daniel,Carlos Lopez-Franco
Publisher : Academic Press
Release Date : 2019-03-15
Category : Science
Total pages :224
GET BOOK

Artificial Neural Networks for Engineering Applications presents current trends for the solution of complex engineering problems that cannot be solved through conventional methods. The proposed methodologies can be applied to modeling, pattern recognition, classification, forecasting, estimation, and more. Readers will find different methodologies to solve various problems, including complex nonlinear systems, cellular computational networks, waste water treatment, attack detection on cyber-physical systems, control of UAVs, biomechanical and biomedical systems, time series forecasting, biofuels, and more. Besides the real-time implementations, the book contains all the theory required to use the proposed methodologies for different applications. Presents the current trends for the solution of complex engineering problems that cannot be solved through conventional methods Includes real-life scenarios where a wide range of artificial neural network architectures can be used to solve the problems encountered in engineering Contains all the theory required to use the proposed methodologies for different applications

Artificial Neural Networks

Artificial Neural Networks
Author : Kenji Suzuki
Publisher : BoD – Books on Demand
Release Date : 2011-04-04
Category : Computers
Total pages :492
GET BOOK

Artificial 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. The purpose of this book is to provide recent advances of artificial neural networks in industrial and control engineering applications. The book begins with a review of applications of artificial neural networks in textile industries. Particular applications in textile industries follow. Parts continue with applications in materials science and industry such as material identification, and estimation of material property and state, food industry such as meat, electric and power industry such as batteries and power systems, mechanical engineering such as engines and machines, and control and robotic engineering such as system control and identification, fault diagnosis systems, and robot manipulation. Thus, this book will be a fundamental source of recent advances and applications of artificial neural networks in industrial and control engineering areas. The target audience includes professors and students in engineering schools, and researchers and engineers in industries.

Neural Network Applications in Control

Neural Network Applications in Control
Author : Institution of Electrical Engineers
Publisher : IET
Release Date : 1995
Category : Technology & Engineering
Total pages :295
GET BOOK

Introducing a wide variety of network types, including Kohenen nets, n-tuple nets and radial basis function networks as well as the more useful multilayer perception back-propagation networks, this book aims to give a detailed appreciation of the use of neural nets in these applications.

Engineering Applications of Neural Networks

Engineering Applications of Neural Networks
Author : Giacomo Boracchi,Lazaros Iliadis,Chrisina Jayne,Aristidis Likas
Publisher : Springer
Release Date : 2017-07-30
Category : Computers
Total pages :737
GET BOOK

This book constitutes the refereed proceedings of the 18th International Conference on Engineering Applications of Neural Networks, EANN 2017, held in Athens, Greece, in August 2017. The 40 revised full papers and 5 revised short papers presented were carefully reviewed and selected from 83 submissions. The papers cover the topics of deep learning, convolutional neural networks, image processing, pattern recognition, recommendation systems, machine learning, and applications of Artificial Neural Networks (ANN) applications in engineering, 5G telecommunication networks, and audio signal processing. The volume also includes papers presented at the 6th Mining Humanistic Data Workshop (MHDW 2017) and the 2nd Workshop on 5G-Putting Intelligence to the Network Edge (5G-PINE).

Artificial Neural Network Applications in Business and Engineering

Artificial Neural Network Applications in Business and Engineering
Author : Quang Hung Do
Publisher : Engineering Science Reference
Release Date : 2020
Category : Business
Total pages :280
GET BOOK

"This book provides recent advances and achievements in the application of artificial neural networks in business and engineering"--

Artificial Neural Networks for Civil Engineers

Artificial Neural Networks for Civil Engineers
Author : Nabil Kartam,Ian Flood,James H. Garrett,Girish Agrawal
Publisher : American Society of Civil Engineers
Release Date : 1997-01-01
Category : Technology & Engineering
Total pages :216
GET BOOK

This monograph provides researchers with an understanding of the potential of artificial neural networks for solving civil engineering related problems, and guidance on how to develop successful implementations for a broad range of problems. Fundamental issues in the selection, development, and use of neural networks, as well as example applications to each of the various disciplines in civil engineering are presented. An introduction to neural networks is provided, along with a classification of the various forms of neural networking systems available (architectures, modes of operation, and methods of development).

Engineering Applications of Neural Networks

Engineering Applications of Neural Networks
Author : Lazaros S. Iliadis,Harris Papadopoulos,Chrisina Jayne
Publisher : Springer
Release Date : 2013-09-25
Category : Computers
Total pages :510
GET BOOK

The two volumes set, CCIS 383 and 384, constitutes the refereed proceedings of the 14th International Conference on Engineering Applications of Neural Networks, EANN 2013, held on Halkidiki, Greece, in September 2013. The 91 revised full papers presented were carefully reviewed and selected from numerous submissions. The papers describe the applications of artificial neural networks and other soft computing approaches to various fields such as pattern recognition-predictors, soft computing applications, medical applications of AI, fuzzy inference, evolutionary algorithms, classification, learning and data mining, control techniques-aspects of AI evolution, image and video analysis, classification, pattern recognition, social media and community based governance, medical applications of AI-bioinformatics and learning.

Artificial Neural Networks for Engineering Applications

Artificial Neural Networks for Engineering Applications
Author : Alma Y. Alanis,Nancy Arana-Daniel,Carlos Lopez-Franco
Publisher : Academic Press
Release Date : 2019-03-15
Category : Science
Total pages :224
GET BOOK

Artificial Neural Networks for Engineering Applications presents current trends for the solution of complex engineering problems that cannot be solved through conventional methods. The proposed methodologies can be applied to modeling, pattern recognition, classification, forecasting, estimation, and more. Readers will find different methodologies to solve various problems, including complex nonlinear systems, cellular computational networks, waste water treatment, attack detection on cyber-physical systems, control of UAVs, biomechanical and biomedical systems, time series forecasting, biofuels, and more. Besides the real-time implementations, the book contains all the theory required to use the proposed methodologies for different applications. Presents the current trends for the solution of complex engineering problems that cannot be solved through conventional methods Includes real-life scenarios where a wide range of artificial neural network architectures can be used to solve the problems encountered in engineering Contains all the theory required to use the proposed methodologies for different applications

Engineering Applications of Bio-Inspired Artificial Neural Networks

Engineering Applications of Bio-Inspired Artificial Neural Networks
Author : Jose Mira,Juan V. Sanchez-Andres
Publisher : Springer Science & Business Media
Release Date : 1999-05-19
Category : Computers
Total pages :912
GET BOOK

This book constitutes, together with its compagnion LNCS 1606, the refereed proceedings of the International Work-Conference on Artificial and Neural Networks, IWANN'99, held in Alicante, Spain in June 1999. The 91 revised papers presented were carefully reviewed and selected for inclusion in the book. This volume is devoted to applications of biologically inspired artificial neural networks in various engineering disciplines. The papers are organized in parts on artificial neural nets simulation and implementation, image processing, and engineering applications.

Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference

Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference
Author : Lazaros Iliadis
Publisher : Springer Nature
Release Date : 2021
Category :
Total pages :129
GET BOOK

Neural Networks for Applied Sciences and Engineering

Neural Networks for Applied Sciences and Engineering
Author : Sandhya Samarasinghe
Publisher : CRC Press
Release Date : 2016-04-19
Category : Computers
Total pages :570
GET BOOK

In response to the exponentially increasing need to analyze vast amounts of data, Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition provides scientists with a simple but systematic introduction to neural networks. Beginning with an introductory discussion on the role of neural networks in scientific data analysis, this book provides a solid foundation of basic neural network concepts. It contains an overview of neural network architectures for practical data analysis followed by extensive step-by-step coverage on linear networks, as well as, multi-layer perceptron for nonlinear prediction and classification explaining all stages of processing and model development illustrated through practical examples and case studies. Later chapters present an extensive coverage on Self Organizing Maps for nonlinear data clustering, recurrent networks for linear nonlinear time series forecasting, and other network types suitable for scientific data analysis. With an easy to understand format using extensive graphical illustrations and multidisciplinary scientific context, this book fills the gap in the market for neural networks for multi-dimensional scientific data, and relates neural networks to statistics. Features § Explains neural networks in a multi-disciplinary context § Uses extensive graphical illustrations to explain complex mathematical concepts for quick and easy understanding ? Examines in-depth neural networks for linear and nonlinear prediction, classification, clustering and forecasting § Illustrates all stages of model development and interpretation of results, including data preprocessing, data dimensionality reduction, input selection, model development and validation, model uncertainty assessment, sensitivity analyses on inputs, errors and model parameters Sandhya Samarasinghe obtained her MSc in Mechanical Engineering from Lumumba University in Russia and an MS and PhD in Engineering from Virginia Tech, USA. Her neural networks research focuses on theoretical understanding and advancements as well as practical implementations.

Engineering Applications of Neural Networks

Engineering Applications of Neural Networks
Author : John Macintyre,Lazaros Iliadis,Ilias Maglogiannis,Chrisina Jayne
Publisher : Springer
Release Date : 2019-05-14
Category : Computers
Total pages :546
GET BOOK

This book constitutes the refereed proceedings of the 19th International Conference on Engineering Applications of Neural Networks, EANN 2019, held in Xersonisos, Crete, Greece, in May 2019. The 35 revised full papers and 5 revised short papers presented were carefully reviewed and selected from 72 submissions. The papers are organized in topical sections on AI in energy management - industrial applications; biomedical - bioinformatics modeling; classification - learning; deep learning; deep learning - convolutional ANN; fuzzy - vulnerability - navigation modeling; machine learning modeling - optimization; ML - DL financial modeling; security - anomaly detection; 1st PEINT workshop.

Engineering Applications of Neural Networks

Engineering Applications of Neural Networks
Author : Elias Pimenidis,Chrisina Jayne
Publisher : Springer
Release Date : 2018-08-20
Category : Computers
Total pages :265
GET BOOK

This book constitutes the refereed proceedings of the 19th International Conference on Engineering Applications of Neural Networks, EANN 2018, held in Bristol, UK, in September 2018. The 16 revised full papers and 5 revised short papers presented were carefully reviewed and selected from 39 submissions. The papers are organized in topical sections on activity recognition, deep learning, extreme learning machine, machine learning applications, predictive models, fuzzy and recommender systems, recurrent neural networks, spiking neural networks.

Engineering Applications of FPGAs

Engineering Applications of FPGAs
Author : Esteban Tlelo-Cuautle,José de Jesús Rangel-Magdaleno,Luis Gerardo de la Fraga
Publisher : Springer
Release Date : 2016-05-28
Category : Technology & Engineering
Total pages :222
GET BOOK

This book offers readers a clear guide to implementing engineering applications with FPGAs, from the mathematical description to the hardware synthesis, including discussion of VHDL programming and co-simulation issues. Coverage includes FPGA realizations such as: chaos generators that are described from their mathematical models; artificial neural networks (ANNs) to predict chaotic time series, for which a discussion of different ANN topologies is included, with different learning techniques and activation functions; random number generators (RNGs) that are realized using different chaos generators, and discussions of their maximum Lyapunov exponent values and entropies. Finally, optimized chaotic oscillators are synchronized and realized to implement a secure communication system that processes black and white and grey-scale images. In each application, readers will find VHDL programming guidelines and computer arithmetic issues, along with co-simulation examples with Active-HDL and Simulink. The whole book provides a practical guide to implementing a variety of engineering applications from VHDL programming and co-simulation issues, to FPGA realizations of chaos generators, ANNs for chaotic time-series prediction, RNGs and chaotic secure communications for image transmission.

Artificial Neural Networks

Artificial Neural Networks
Author : Nicolaos Karayiannis,Anastasios N. Venetsanopoulos
Publisher : Springer Science & Business Media
Release Date : 2013-06-29
Category : Computers
Total pages :440
GET BOOK

1.1 Overview We are living in a decade recently declared as the "Decade of the Brain". Neuroscientists may soon manage to work out a functional map of the brain, thanks to technologies that open windows on the mind. With the average human brain consisting of 15 billion neurons, roughly equal to the number of stars in our milky way, each receiving signals through as many as 10,000 synapses, it is quite a view. "The brain is the last and greatest biological frontier", says James Weston codiscoverer of DNA, considered to be the most complex piece of biological machinery on earth. After many years of research by neuroanatomists and neurophys iologists, the overall organization of the brain is well understood, but many of its detailed neural mechanisms remain to be decoded. In order to understand the functioning of the brain, neurobiologists have taken a bottom-up approach of studying the stimulus-response characteristics of single neurons and networks of neurons, while psy chologists have taken a top-down approach of studying brain func tions from the cognitive and behavioral level. While these two ap proaches are gradually converging, it is generally accepted that it may take another fifty years before we achieve a solid microscopic, intermediate, and macroscopic understanding of brain.