Artificial Neural Network Modelling

This book PDF is perfect for those who love Technology & Engineering genre, written by Subana Shanmuganathan and published by Springer which was released on 03 February 2016 with total hardcover pages 472. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Artificial Neural Network Modelling books below.

Artificial Neural Network Modelling

Author	: Subana Shanmuganathan
File Size	: 47,8 Mb
Publisher	: Springer
Language	: English
Release Date	: 03 February 2016
ISBN	: 9783319284958
Pages	: 472 pages

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Artificial Neural Network Modelling by Subana Shanmuganathan Book PDF Summary

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 by Subana Shanmuganathan,Sandhya Samarasinghe

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Artificial Neural Network Modelling by Subana Shanmuganathan,Sandhya Samarasinghe

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Artificial Neural Networks for Modelling and Control of Non Linear Systems by Johan A.K. Suykens,Joos P.L. Vandewalle,B.L. de Moor

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

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Mastering Machine Learning Algorithms by Giuseppe Bonaccorso

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

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Artificial Neural Networks by Joao Luis Garcia Rosa

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

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Forecasting principles and practice by Rob J Hyndman,George Athanasopoulos

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

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Neural Network Models by Philippe de Wilde

Providing an in-depth treatment of neural network models, this volume explains and proves the main results in a clear and accessible way. It presents the essential principles of nonlinear dynamics as derived from neurobiology, and investigates the stability, convergence behaviour and capacity of networks.

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A Comprehensive Guide to Neural Network Modeling by Steffen Skaar

As artificial neural networks have been gaining importance in the field of engineering, this compilation aims to review the scientific literature regarding the use of artificial neural networks for the modelling and optimization of food drying processes. The applications of artificial neural networks in food engineering are presented, particularly focusing

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