Artificial Neural Networks for Engineering Applications

This book PDF is perfect for those who love Science genre, written by Alma Y. Alanis and published by Academic Press which was released on 15 March 2019 with total hardcover pages 176. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Artificial Neural Networks for Engineering Applications books below.

Artificial Neural Networks for Engineering Applications
Author : Alma Y. Alanis
File Size : 52,9 Mb
Publisher : Academic Press
Language : English
Release Date : 15 March 2019
ISBN : 9780128182475
Pages : 176 pages
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Artificial Neural Networks for Engineering Applications by Alma Y. Alanis Book PDF Summary

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 for Engineering Applications

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

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