Neural networks are inherently parallel algorithms. Multicore CPUs, graphical processing units (GPUs), and clusters of computers with multiple CPUs and GPUs can take advantage of this parallelism. Parallel Computing Toolbox, when used in conjunction with Neural Network Toolbox, enables neural network training and simulation to take advantage of each mode of parallelism. Parallel Computing Toolbox allows neural network training and simulation to run across multiple CPU cores on a single PC, or across multiple CPUs on multiple computers on a network using MATLAB Distributed Computing Server. Using multiple cores can speed calculations. Using multiple computers can allow you to solve problems using data sets too big to fit in the RAM of a single computer. The only limit to problem size is the total quantity of RAM available across all computers. Distributed and GPU computing can be combined to run calculations across multiple CPUs and/or GPUs on a single computer, or on a cluster with MATLAB Distributed Computing Server. It is desirable to determine the optimal regularization parameters in an automated fashion. One approach to this process is the Bayesian framework. In this framework, the weights and biases of the network are assumed to be random variables with specified distributions. The regularization parameters are related to the unknown variances associated with these distributions. You can then estimate these parameters using statistical techniques. It is very difficult to know which training algorithm will be the fastest for a given problem. It depends on many factors, including the complexity of the problem, the number of data points in the training set, the number of weights and biases in the network, the error goal, and whether the network is being used for pattern recognition (discriminant analysis) or function approximation (regression). This book compares the various training algorithms. One of the problems that occur during neural network training is called overfitting. The error on the training set is driven to a very small value, but when new data is presented to the network the error is large. The network has memorized the training examples, but it has not learned to generalize to new situations. This book develops the following topics: Neural Networks with Parallel and GPU Computing Deep Learning Optimize Neural Network Training Speed and Memory Improve Neural Network Generalization and Avoid Overfitting Create and Train Custom Neural Network Architectures Deploy Training of Neural Networks Perceptron Neural Networks Linear Neural Networks Hopfield Neural Network Neural Network Object Reference Neural Network Simulink Block Library Deploy Neural Network Simulink Diagrams

The Special Issue Machining—Recent Advances, Applications and Challenges is intended as a humble collection of some of the hottest topics in machining. The manufacturing industry is a varying and challenging environment where new advances emerge from one day to another. In recent years, new manufacturing procedures have retained increasing attention from the industrial and scientific community. However, machining still remains the key operation to achieve high productivity and precision for high-added value parts. Continuous research is performed, and new ideas are constantly considered. This Special Issue summarizes selected high-quality papers which were submitted, peer-reviewed, and recommended by experts. It covers some (but not only) of the following topics: High performance operations for difficult-to-cut alloys, wrought and cast materials, light alloys, ceramics, etc.; Cutting tools, grades, substrates and coatings. Wear damage; Advanced cooling in machining: Minimum quantity of lubricant, dry or cryogenics; Modelling, focused on the reduction of risks, the process outcome, and to maintain surface integrity; Vibration problems in machines: Active and passive/predictive methods, sources, diagnosis and avoidance; Influence of machining in new concepts of machine–tool, and machine static and dynamic behaviors; Machinability of new composites, brittle and emerging materials; Assisted machining processes by high-pressure, laser, US, and others; Introduction of new analytics and decision making into machining programming. We wish to thank the reviewers and staff from Materials for their comments, advice, suggestions and invaluable support during the development of this Special Issue.

This book constitutes the proceedings of the 11th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2014, held in Ilan, Taiwan, in September 2014. The 42 full papers and 24 poster papers presented were carefully reviewed and selected from 196 submissions. They are organized in topical sections on systems, networks, and architectures, parallel and multi-core technologies, virtualization and cloud computing technologies, applications of parallel and distributed computing, and I/O, file systems, and data management.

Advances in Time-Domain Computational Electromagnetic Methods Discover state-of-the-art time domain electromagnetic modeling and simulation algorithms Advances in Time-Domain Computational Electromagnetic Methods delivers a thorough exploration of recent developments in time domain computational methods for solving complex electromagnetic problems. The book discusses the main time domain computational electromagnetics techniques, including finite-difference time domain (FDTD), finite-element time domain (FETD), discontinuous Galerkin time domain (DGTD), time domain integral equation (TDIE), and other methods in electromagnetic, multiphysics modeling and simulation, and antenna designs. The book bridges the gap between academic research and real engineering applications by comprehensively surveying the full picture of current state-of-the-art time domain electromagnetic simulation techniques. Among other topics, it offers readers discussions of automatic load balancing schemes for DG-FETD/SETD methods and convolution quadrature time domain integral equation methods for electromagnetic scattering. Advances in Time-Domain Computational Electromagnetic Methods also includes: Introductions to cylindrical, spherical, and symplectic FDTD, as well as FDTD for metasurfaces with GSTC and FDTD for nonlinear metasurfaces Explorations of FETD for dispersive and nonlinear media and SETD-DDM for periodic/ quasi-periodic arrays Discussions of TDIE, including explicit marching-on-in-time solvers for second-kind time domain integral equations, TD-SIE DDM, and convolution quadrature time domain integral equation methods for electromagnetic scattering Treatments of deep learning, including time domain electromagnetic forward and inverse modeling using a differentiable programming platform Ideal for undergraduate and graduate students studying the design and development of various kinds of communication systems, as well as professionals working in these fields, Advances in Time-Domain Computational Electromagnetic Methods is also an invaluable resource for those taking advanced graduate courses in computational electromagnetic methods and simulation techniques.

GPU programming in MATLAB is intended for scientists, engineers, or students who develop or maintain applications in MATLAB and would like to accelerate their codes using GPU programming without losing the many benefits of MATLAB. The book starts with coverage of the Parallel Computing Toolbox and other MATLAB toolboxes for GPU computing, which allow applications to be ported straightforwardly onto GPUs without extensive knowledge of GPU programming. The next part covers built-in, GPU-enabled features of MATLAB, including options to leverage GPUs across multicore or different computer systems. Finally, advanced material includes CUDA code in MATLAB and optimizing existing GPU applications. Throughout the book, examples and source codes illustrate every concept so that readers can immediately apply them to their own development. - Provides in-depth, comprehensive coverage of GPUs with MATLAB, including the parallel computing toolbox and built-in features for other MATLAB toolboxes - Explains how to accelerate computationally heavy applications in MATLAB without the need to re-write them in another language - Presents case studies illustrating key concepts across multiple fields - Includes source code, sample datasets, and lecture slides

Praise from the Second Edition "...an excellent introduction to optimization theory..." (Journal of Mathematical Psychology, 2002) "A textbook for a one-semester course on optimization theory and methods at the senior undergraduate or beginning graduate level." (SciTech Book News, Vol. 26, No. 2, June 2002) Explore the latest applications of optimization theory and methods Optimization is central to any problem involving decision making in many disciplines, such as engineering, mathematics, statistics, economics, and computer science. Now, more than ever, it is increasingly vital to have a firm grasp of the topic due to the rapid progress in computer technology, including the development and availability of user-friendly software, high-speed and parallel processors, and networks. Fully updated to reflect modern developments in the field, An Introduction to Optimization, Third Edition fills the need for an accessible, yet rigorous, introduction to optimization theory and methods. The book begins with a review of basic definitions and notations and also provides the related fundamental background of linear algebra, geometry, and calculus. With this foundation, the authors explore the essential topics of unconstrained optimization problems, linear programming problems, and nonlinear constrained optimization. An optimization perspective on global search methods is featured and includes discussions on genetic algorithms, particle swarm optimization, and the simulated annealing algorithm. In addition, the book includes an elementary introduction to artificial neural networks, convex optimization, and multi-objective optimization, all of which are of tremendous interest to students, researchers, and practitioners. Additional features of the Third Edition include: New discussions of semidefinite programming and Lagrangian algorithms A new chapter on global search methods A new chapter on multipleobjective optimization New and modified examples and exercises in each chapter as well as an updated bibliography containing new references An updated Instructor's Manual with fully worked-out solutions to the exercises Numerous diagrams and figures found throughout the text complement the written presentation of key concepts, and each chapter is followed by MATLAB exercises and drill problems that reinforce the discussed theory and algorithms. With innovative coverage and a straightforward approach, An Introduction to Optimization, Third Edition is an excellent book for courses in optimization theory and methods at the upper-undergraduate and graduate levels. It also serves as a useful, self-contained reference for researchers and professionals in a wide array of fields.

THE AUTHOR(S) AND PUBLISHER OF THIS BOOK HAVE USED THEIR BEST EFFORTS IN PREPARING THIS BOOK. THESE EFFORTS INCLUDE THE DEVELOPMENT, RESEARCH ANDTESTING OF THE THEORIES AND PROGRAMS TO DETERMINE THEIR EFFECTIVENESS. THE AUTHORS AND PUBLISHER MAKES NO WARRANTY OF ANY KIND, EXPRESSED OR IMPLIEDWITH REGARD TO THESE PROGRAMS OR THE DOCUMENTATION CONTAINED IN THIS BOOK. THE AUTHORS AND PUBLISHER SHALL NOT BE LIABLE IN ANY EVENT FORINCIDENTAL OR CONSEQUENTIAL DAMAGES IN CONNECTION WITH, OR ARISING OUT OF, THE FURNISHING, PERFORMANCE, OR USE OF THESE PROGRAMS. COPYRIGHTS © 2023 BY MILESTONE RESEARCH PUBLICATIONS, INC. THIS EDITION IS PUBLISHED BY ARRANGEMENT WITH MILESTONE RESEARCH FOUNDATION, INC. THIS BOOK IS SOLD SUBJECT TO THE CONDITION THAT IT SHALL NOT, BY WAY OF TRADE OR OTHERWISE, BE LENT, RESOLD, HIRED OUT, OR OTHERWISE CIRCULATED WITHOUTTHE PUBLISHER'S PRIOR WRITTEN CONSENT IN ANY FORM OF BINDING OR COVER OTHER THAN THAT IN WHICH IT IS PUBLISHED AND WITHOUT A SIMILAR CONDITIONINCLUDING THIS CONDITION BEING IMPOSED ON THE SUBSEQUENT PURCHASER AND WITHOUT LIMITING THE RIGHTS UNDER COPYRIGHT RESERVED ABOVE, NO PART OF THISPUBLICATION MAY BE REPRODUCED, STORED IN OR INTRODUCED INTO RETRIEVAL SYSTEM, OR TRANSMITTED IN ANY FORM OR BY ANY MEANS (ELECTRONIC, MECHANICAL,PHOTOCOPYING, RECORDING AND OTHERWISE) WITHOUT THE PRIOR WRITTEN PERMISSION OF BOTH THE COPYRIGHT OWNER AND THE ABOVE MENTIONED PUBLISHER OFTHIS BOOK.