Neural Networks for Applied Sciences and Engineering

Neural Networks for Applied Sciences and Engineering
Author: Sandhya Samarasinghe
Publisher: CRC Press
Total Pages: 596
Release: 2016-04-19
Genre: Computers
ISBN: 1420013068

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

Applied Artificial Neural Network Methods For Engineers And Scientists: Solving Algebraic Equations

Applied Artificial Neural Network Methods For Engineers And Scientists: Solving Algebraic Equations
Author: Snehashish Chakraverty
Publisher: World Scientific
Total Pages: 192
Release: 2021-01-26
Genre: Computers
ISBN: 9811230226

The aim of this book is to handle different application problems of science and engineering using expert Artificial Neural Network (ANN). As such, the book starts with basics of ANN along with different mathematical preliminaries with respect to algebraic equations. Then it addresses ANN based methods for solving different algebraic equations viz. polynomial equations, diophantine equations, transcendental equations, system of linear and nonlinear equations, eigenvalue problems etc. which are the basic equations to handle the application problems mentioned in the content of the book. Although there exist various methods to handle these problems, but sometimes those may be problem dependent and may fail to give a converge solution with particular discretization. Accordingly, ANN based methods have been addressed here to solve these problems. Detail ANN architecture with step by step procedure and algorithm have been included. Different example problems are solved with respect to various application and mathematical problems. Convergence plots and/or convergence tables of the solutions are depicted to show the efficacy of these methods. It is worth mentioning that various application problems viz. Bakery problem, Power electronics applications, Pole placement, Electrical Network Analysis, Structural engineering problem etc. have been solved using the ANN based methods.

Artificial Neural Networks for Engineering Applications

Artificial Neural Networks for Engineering Applications
Author: Alma Y Alanis
Publisher: Academic Press
Total Pages: 176
Release: 2019-02-13
Genre: Science
ISBN: 0128182474

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.

Artificial Neural Networks for Engineers and Scientists

Artificial Neural Networks for Engineers and Scientists
Author: S. Chakraverty
Publisher: CRC Press
Total Pages: 157
Release: 2017-07-20
Genre: Mathematics
ISBN: 1351651315

Differential equations play a vital role in the fields of engineering and science. Problems in engineering and science can be modeled using ordinary or partial differential equations. Analytical solutions of differential equations may not be obtained easily, so numerical methods have been developed to handle them. Machine intelligence methods, such as Artificial Neural Networks (ANN), are being used to solve differential equations, and these methods are presented in Artificial Neural Networks for Engineers and Scientists: Solving Ordinary Differential Equations. This book shows how computation of differential equation becomes faster once the ANN model is properly developed and applied.

Artificial Higher Order Neural Networks for Modeling and Simulation

Artificial Higher Order Neural Networks for Modeling and Simulation
Author: Zhang, Ming
Publisher: IGI Global
Total Pages: 455
Release: 2012-10-31
Genre: Computers
ISBN: 1466621761

"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.

Data-Driven Science and Engineering

Data-Driven Science and Engineering
Author: Steven L. Brunton
Publisher: Cambridge University Press
Total Pages: 615
Release: 2022-05-05
Genre: Computers
ISBN: 1009098489

A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLABĀ®.

Neural Networks: Computational Models and Applications

Neural Networks: Computational Models and Applications
Author: Huajin Tang
Publisher: Springer Science & Business Media
Total Pages: 310
Release: 2007-03-12
Genre: Computers
ISBN: 3540692258

Neural Networks: Computational Models and Applications presents important theoretical and practical issues in neural networks, including the learning algorithms of feed-forward neural networks, various dynamical properties of recurrent neural networks, winner-take-all networks and their applications in broad manifolds of computational intelligence: pattern recognition, uniform approximation, constrained optimization, NP-hard problems, and image segmentation. The book offers a compact, insightful understanding of the broad and rapidly growing neural networks domain.

Fuzzy Engineering Expert Systems with Neural Network Applications

Fuzzy Engineering Expert Systems with Neural Network Applications
Author: Adedeji Bodunde Badiru
Publisher: John Wiley & Sons
Total Pages: 313
Release: 2002-10-08
Genre: Computers
ISBN: 0471275344

Provides an up-to-date integration of expert systems with fuzzy logic and neural networks. Includes coverage of simulation models not present in other books. Presents cases and examples taken from the authors' experience in research and applying the technology to real-world situations.