Artificial Neural Network Modelling

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Author :
Publisher : Springer
ISBN 13 : 3319284959
Total Pages : 472 pages
Book Rating : 4.58/5 ( download)

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Book Synopsis Artificial Neural Network Modelling by : Subana Shanmuganathan

Download or read book Artificial Neural Network Modelling written by Subana Shanmuganathan and published by Springer. This book was released on 2016-02-03 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Artificial Neural Network Modelling

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Author :
Publisher : Springer
ISBN 13 : 9783319284934
Total Pages : 0 pages
Book Rating : 4.32/5 ( download)

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Book Synopsis Artificial Neural Network Modelling by : Subana Shanmuganathan

Download or read book Artificial Neural Network Modelling written by Subana Shanmuganathan and published by Springer. This book was released on 2016-02-12 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Mastering Machine Learning Algorithms

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Publisher : Packt Publishing Ltd
ISBN 13 : 1788625900
Total Pages : 567 pages
Book Rating : 4.06/5 ( download)

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Book Synopsis Mastering Machine Learning Algorithms by : Giuseppe Bonaccorso

Download or read book Mastering Machine Learning Algorithms written by Giuseppe Bonaccorso and published by Packt Publishing Ltd. This book was released on 2018-05-25 with total page 567 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore and master the most important algorithms for solving complex machine learning problems. Key Features Discover high-performing machine learning algorithms and understand how they work in depth. One-stop solution to mastering supervised, unsupervised, and semi-supervised machine learning algorithms and their implementation. Master concepts related to algorithm tuning, parameter optimization, and more Book Description Machine learning is a subset of AI that aims to make modern-day computer systems smarter and more intelligent. The real power of machine learning resides in its algorithms, which make even the most difficult things capable of being handled by machines. However, with the advancement in the technology and requirements of data, machines will have to be smarter than they are today to meet the overwhelming data needs; mastering these algorithms and using them optimally is the need of the hour. Mastering Machine Learning Algorithms is your complete guide to quickly getting to grips with popular machine learning algorithms. You will be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and will learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this book will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries such as scikit-learn. You will also learn how to use Keras and TensorFlow to train effective neural networks. If you are looking for a single resource to study, implement, and solve end-to-end machine learning problems and use-cases, this is the book you need. What you will learn Explore how a ML model can be trained, optimized, and evaluated Understand how to create and learn static and dynamic probabilistic models Successfully cluster high-dimensional data and evaluate model accuracy Discover how artificial neural networks work and how to train, optimize, and validate them Work with Autoencoders and Generative Adversarial Networks Apply label spreading and propagation to large datasets Explore the most important Reinforcement Learning techniques Who this book is for This book is an ideal and relevant source of content for data science professionals who want to delve into complex machine learning algorithms, calibrate models, and improve the predictions of the trained model. A basic knowledge of machine learning is preferred to get the best out of this guide.

Artificial Neural Networks for Modelling and Control of Non-Linear Systems

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Author :
Publisher : Springer Science & Business Media
ISBN 13 : 1475724934
Total Pages : 242 pages
Book Rating : 4.36/5 ( download)

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Book Synopsis Artificial Neural Networks for Modelling and Control of Non-Linear Systems by : Johan A.K. Suykens

Download or read book Artificial Neural Networks for Modelling and Control of Non-Linear Systems written by Johan A.K. Suykens and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial neural networks possess several properties that make them particularly attractive for applications to modelling and control of complex non-linear systems. Among these properties are their universal approximation ability, their parallel network structure and the availability of on- and off-line learning methods for the interconnection weights. However, dynamic models that contain neural network architectures might be highly non-linear and difficult to analyse as a result. Artificial Neural Networks for Modelling and Control of Non-Linear Systems investigates the subject from a system theoretical point of view. However the mathematical theory that is required from the reader is limited to matrix calculus, basic analysis, differential equations and basic linear system theory. No preliminary knowledge of neural networks is explicitly required. The book presents both classical and novel network architectures and learning algorithms for modelling and control. Topics include non-linear system identification, neural optimal control, top-down model based neural control design and stability analysis of neural control systems. A major contribution of this book is to introduce NLq Theory as an extension towards modern control theory, in order to analyze and synthesize non-linear systems that contain linear together with static non-linear operators that satisfy a sector condition: neural state space control systems are an example. Moreover, it turns out that NLq Theory is unifying with respect to many problems arising in neural networks, systems and control. Examples show that complex non-linear systems can be modelled and controlled within NLq theory, including mastering chaos. The didactic flavor of this book makes it suitable for use as a text for a course on Neural Networks. In addition, researchers and designers will find many important new techniques, in particular NLq emTheory, that have applications in control theory, system theory, circuit theory and Time Series Analysis.

Artificial Higher Order Neural Networks for Modeling and Simulation

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Author :
Publisher : IGI Global
ISBN 13 : 1466621761
Total Pages : 455 pages
Book Rating : 4.63/5 ( download)

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Book Synopsis Artificial Higher Order Neural Networks for Modeling and Simulation by : Zhang, Ming

Download or read book Artificial Higher Order Neural Networks for Modeling and Simulation written by Zhang, Ming and published by IGI Global. This book was released on 2012-10-31 with total page 455 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book introduces Higher Order Neural Networks (HONNs) to computer scientists and computer engineers as an open box neural networks tool when compared to traditional artificial neural networks"--Provided by publisher.

Forecasting: principles and practice

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Publisher : OTexts
ISBN 13 : 0987507117
Total Pages : 380 pages
Book Rating : 4.12/5 ( download)

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Book Synopsis Forecasting: principles and practice by : Rob J Hyndman

Download or read book Forecasting: principles and practice written by Rob J Hyndman and published by OTexts. This book was released on 2018-05-08 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly.

Artificial Neural Networks

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Publisher : BoD – Books on Demand
ISBN 13 : 9535127047
Total Pages : 416 pages
Book Rating : 4.48/5 ( download)

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Book Synopsis Artificial Neural Networks by : Joao Luis Garcia Rosa

Download or read book Artificial Neural Networks written by Joao Luis Garcia Rosa and published by BoD – Books on Demand. This book was released on 2016-10-19 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: The idea of simulating the brain was the goal of many pioneering works in Artificial Intelligence. The brain has been seen as a neural network, or a set of nodes, or neurons, connected by communication lines. Currently, there has been increasing interest in the use of neural network models. This book contains chapters on basic concepts of artificial neural networks, recent connectionist architectures and several successful applications in various fields of knowledge, from assisted speech therapy to remote sensing of hydrological parameters, from fabric defect classification to application in civil engineering. This is a current book on Artificial Neural Networks and Applications, bringing recent advances in the area to the reader interested in this always-evolving machine learning technique.

Advances in Computational Intelligence

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Publisher : Springer Science & Business Media
ISBN 13 : 3642215009
Total Pages : 601 pages
Book Rating : 4.01/5 ( download)

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Book Synopsis Advances in Computational Intelligence by : Joan Cabestany

Download or read book Advances in Computational Intelligence written by Joan Cabestany and published by Springer Science & Business Media. This book was released on 2011-05-30 with total page 601 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set LNCS 6691 and 6692 constitutes the refereed proceedings of the 11th International Work-Conference on Artificial Neural Networks, IWANN 2011, held in Torremolinos-Málaga, Spain, in June 2011. The 154 revised papers were carefully reviewed and selected from 202 submissions for presentation in two volumes. The first volume includes 69 papers organized in topical sections on mathematical and theoretical methods in computational intelligence; learning and adaptation; bio-inspired systems and neuro-engineering; hybrid intelligent systems; applications of computational intelligence; new applications of brain-computer interfaces; optimization algorithms in graphic processing units; computing languages with bio-inspired devices and multi-agent systems; computational intelligence in multimedia processing; and biologically plausible spiking neural processing.

Artificial Neural Network Modeling of Water and Wastewater Treatment Processes

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Author :
Publisher : Nova Novinka
ISBN 13 : 9781611227819
Total Pages : 0 pages
Book Rating : 4.1X/5 ( download)

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Book Synopsis Artificial Neural Network Modeling of Water and Wastewater Treatment Processes by : Ali Reza Khataee

Download or read book Artificial Neural Network Modeling of Water and Wastewater Treatment Processes written by Ali Reza Khataee and published by Nova Novinka. This book was released on 2011 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial neural networks (ANNs) are computer based systems that are designed to simulate the learning process of neurons in the human brain. ANNs have been attracting great interest during the last decade as predictive models and pattern recognition. Artificial neural networks possess the ability to "learn" from a set of experimental data without actual knowledge of the physical and chemical laws that govern the system. Therefore, ANNs application in data treatment is high, especially where systems present non-linearities and complex behaviour. This book describes the application of artificial neural networks for modelling of water and wastewater treatment processes.

Research Anthology on Artificial Neural Network Applications

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Author :
Publisher : IGI Global
ISBN 13 : 1668424096
Total Pages : 1575 pages
Book Rating : 4.94/5 ( download)

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Book Synopsis Research Anthology on Artificial Neural Network Applications by : Management Association, Information Resources

Download or read book Research Anthology on Artificial Neural Network Applications written by Management Association, Information Resources and published by IGI Global. This book was released on 2021-07-16 with total page 1575 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial neural networks (ANNs) present many benefits in analyzing complex data in a proficient manner. As an effective and efficient problem-solving method, ANNs are incredibly useful in many different fields. From education to medicine and banking to engineering, artificial neural networks are a growing phenomenon as more realize the plethora of uses and benefits they provide. Due to their complexity, it is vital for researchers to understand ANN capabilities in various fields. The Research Anthology on Artificial Neural Network Applications covers critical topics related to artificial neural networks and their multitude of applications in a number of diverse areas including medicine, finance, operations research, business, social media, security, and more. Covering everything from the applications and uses of artificial neural networks to deep learning and non-linear problems, this book is ideal for computer scientists, IT specialists, data scientists, technologists, business owners, engineers, government agencies, researchers, academicians, and students, as well as anyone who is interested in learning more about how artificial neural networks can be used across a wide range of fields.