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GPU Programming in MATLAB

GPU Programming in MATLAB Book
Author : Nikolaos Ploskas,Nikolaos Samaras
Publisher : Morgan Kaufmann
Release : 2016-08-25
ISBN : 0128051337
Language : En, Es, Fr & De

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Book Description :

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

Accelerating MATLAB with GPU Computing

Accelerating MATLAB with GPU Computing Book
Author : Jung W. Suh,Youngmin Kim
Publisher : Morgan Kaufmann
Release : 2013-11-26
ISBN : 9780124080805
Language : En, Es, Fr & De

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Book Description :

Beyond simulation and algorithm development, many developers increasingly use MATLAB even for product deployment in computationally heavy fields. This often demands that MATLAB codes run faster by leveraging the distributed parallelism of Graphics Processing Units (GPUs). While MATLAB successfully provides high-level functions as a simulation tool for rapid prototyping, the underlying details and knowledge needed for utilizing GPUs make MATLAB users hesitate to step into it. Accelerating MATLAB with GPUs offers a primer on bridging this gap. Starting with the basics, setting up MATLAB for CUDA (in Windows, Linux and Mac OS X) and profiling, it then guides users through advanced topics such as CUDA libraries. The authors share their experience developing algorithms using MATLAB, C++ and GPUs for huge datasets, modifying MATLAB codes to better utilize the computational power of GPUs, and integrating them into commercial software products. Throughout the book, they demonstrate many example codes that can be used as templates of C-MEX and CUDA codes for readers' projects. Download example codes from the publisher's website: http://booksite.elsevier.com/9780124080805/ Shows how to accelerate MATLAB codes through the GPU for parallel processing, with minimal hardware knowledge Explains the related background on hardware, architecture and programming for ease of use Provides simple worked examples of MATLAB and CUDA C codes as well as templates that can be reused in real-world projects

MATLAB

MATLAB Book
Author : Kelly Bennett
Publisher : BoD – Books on Demand
Release : 2014-09-08
ISBN : 953511719X
Language : En, Es, Fr & De

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Book Description :

MATLAB is an indispensable asset for scientists, researchers, and engineers. The richness of the MATLAB computational environment combined with an integrated development environment (IDE) and straightforward interface, toolkits, and simulation and modeling capabilities, creates a research and development tool that has no equal. From quick code prototyping to full blown deployable applications, MATLAB stands as a de facto development language and environment serving the technical needs of a wide range of users. As a collection of diverse applications, each book chapter presents a novel application and use of MATLAB for a specific result.

Recent Progress in Parallel and Distributed Computing

Recent Progress in Parallel and Distributed Computing Book
Author : Wen-Jyi Hwang
Publisher : BoD – Books on Demand
Release : 2017-07-19
ISBN : 9535133152
Language : En, Es, Fr & De

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Book Description :

Parallel and distributed computing has been one of the most active areas of research in recent years. The techniques involved have found significant applications in areas as diverse as engineering, management, natural sciences, and social sciences. This book reports state-of-the-art topics and advances in this emerging field. Completely up-to-date, aspects it examines include the following: 1) Social networks; 2) Smart grids; 3) Graphic processing unit computation; 4) Distributed software development tools; 5) Analytic hierarchy process and the analytic network process

Accelerating MATLAB Performance

Accelerating MATLAB Performance Book
Author : Yair M. Altman
Publisher : CRC Press
Release : 2014-12-11
ISBN : 1482211297
Language : En, Es, Fr & De

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Book Description :

The MATLAB® programming environment is often perceived as a platform suitable for prototyping and modeling but not for "serious" applications. One of the main complaints is that MATLAB is just too slow. Accelerating MATLAB Performance aims to correct this perception by describing multiple ways to greatly improve MATLAB program speed. Packed with thousands of helpful tips, it leaves no stone unturned, discussing every aspect of MATLAB. Ideal for novices and professionals alike, the book describes MATLAB performance in a scale and depth never before published. It takes a comprehensive approach to MATLAB performance, illustrating numerous ways to attain the desired speedup. The book covers MATLAB, CPU, and memory profiling and discusses various tradeoffs in performance tuning. It describes both the application of standard industry techniques in MATLAB, as well as methods that are specific to MATLAB such as using different data types or built-in functions. The book covers MATLAB vectorization, parallelization (implicit and explicit), optimization, memory management, chunking, and caching. It explains MATLAB’s memory model and details how it can be leveraged. It describes the use of GPU, MEX, FPGA, and other forms of compiled code, as well as techniques for speeding up deployed applications. It details specific tips for MATLAB GUI, graphics, and I/O. It also reviews a wide variety of utilities, libraries, and toolboxes that can help to improve performance. Sufficient information is provided to allow readers to immediately apply the suggestions to their own MATLAB programs. Extensive references are also included to allow those who wish to expand the treatment of a particular topic to do so easily. Supported by an active website, and numerous code examples, the book will help readers rapidly attain significant reductions in development costs and program run times.

Accelerating MATLAB with GPU Computing

Accelerating MATLAB with GPU Computing Book
Author : Jung W. Suh,Youngmin Kim
Publisher : Newnes
Release : 2013-11-18
ISBN : 0124079164
Language : En, Es, Fr & De

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Book Description :

Beyond simulation and algorithm development, many developers increasingly use MATLAB even for product deployment in computationally heavy fields. This often demands that MATLAB codes run faster by leveraging the distributed parallelism of Graphics Processing Units (GPUs). While MATLAB successfully provides high-level functions as a simulation tool for rapid prototyping, the underlying details and knowledge needed for utilizing GPUs make MATLAB users hesitate to step into it. Accelerating MATLAB with GPUs offers a primer on bridging this gap. Starting with the basics, setting up MATLAB for CUDA (in Windows, Linux and Mac OS X) and profiling, it then guides users through advanced topics such as CUDA libraries. The authors share their experience developing algorithms using MATLAB, C++ and GPUs for huge datasets, modifying MATLAB codes to better utilize the computational power of GPUs, and integrating them into commercial software products. Throughout the book, they demonstrate many example codes that can be used as templates of C-MEX and CUDA codes for readers’ projects. Download example codes from the publisher's website: http://booksite.elsevier.com/9780124080805/ Shows how to accelerate MATLAB codes through the GPU for parallel processing, with minimal hardware knowledge Explains the related background on hardware, architecture and programming for ease of use Provides simple worked examples of MATLAB and CUDA C codes as well as templates that can be reused in real-world projects

Proceedings of 2013 Chinese Intelligent Automation Conference

Proceedings of 2013 Chinese Intelligent Automation Conference Book
Author : Zengqi Sun,Zhidong Deng
Publisher : Springer Science & Business Media
Release : 2013-07-10
ISBN : 3642384609
Language : En, Es, Fr & De

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Book Description :

Proceedings of the 2013 Chinese Intelligent Automation Conference presents selected research papers from the CIAC’13, held in Yangzhou, China. The topics include e.g. adaptive control, fuzzy control, neural network based control, knowledge based control, hybrid intelligent control, learning control, evolutionary mechanism based control, multi-sensor integration, failure diagnosis, and reconfigurable control. Engineers and researchers from academia, industry, and government can gain an inside view of new solutions combining ideas from multiple disciplines in the field of intelligent automation. Zengqi Sun and Zhidong Deng are professors at the Department of Computer Science, Tsinghua University, China.

GPU Computing Gems Jade Edition

GPU Computing Gems Jade Edition Book
Author : Anonim
Publisher : Elsevier
Release : 2011-11-02
ISBN : 0123859646
Language : En, Es, Fr & De

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Book Description :

GPU Computing Gems, Jade Edition, offers hands-on, proven techniques for general purpose GPU programming based on the successful application experiences of leading researchers and developers. One of few resources available that distills the best practices of the community of CUDA programmers, this second edition contains 100% new material of interest across industry, including finance, medicine, imaging, engineering, gaming, environmental science, and green computing. It covers new tools and frameworks for productive GPU computing application development and provides immediate benefit to researchers developing improved programming environments for GPUs. Divided into five sections, this book explains how GPU execution is achieved with algorithm implementation techniques and approaches to data structure layout. More specifically, it considers three general requirements: high level of parallelism, coherent memory access by threads within warps, and coherent control flow within warps. Chapters explore topics such as accelerating database searches; how to leverage the Fermi GPU architecture to further accelerate prefix operations; and GPU implementation of hash tables. There are also discussions on the state of GPU computing in interactive physics and artificial intelligence; programming tools and techniques for GPU computing; and the edge and node parallelism approach for computing graph centrality metrics. In addition, the book proposes an alternative approach that balances computation regardless of node degree variance. Software engineers, programmers, hardware engineers, and advanced students will find this book extremely useful. This second volume of GPU Computing Gems offers 100% new material of interest across industry, including finance, medicine, imaging, engineering, gaming, environmental science, green computing, and more Covers new tools and frameworks for productive GPU computing application development and offers immediate benefit to researchers developing improved programming environments for GPUs Even more hands-on, proven techniques demonstrating how general purpose GPU computing is changing scientific research Distills the best practices of the community of CUDA programmers; each chapter provides insights and ideas as well as 'hands on' skills applicable to a variety of fields

Geometric Modelling Numerical Simulation and Optimization

Geometric Modelling  Numerical Simulation  and Optimization  Book
Author : Geir Hasle,Knut-Andreas Lie,Ewald Quak
Publisher : Springer Science & Business Media
Release : 2007-06-10
ISBN : 3540687831
Language : En, Es, Fr & De

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Book Description :

This edited volume addresses the importance of mathematics for industry and society by presenting highlights from contract research at the Department of Applied Mathematics at SINTEF, the largest independent research organization in Scandinavia. Examples range from computer-aided geometric design, via general purpose computing on graphics cards, to reservoir simulation for enhanced oil recovery. Contributions are written in a tutorial style.

Handbook Of Financial Econometrics Mathematics Statistics And Machine Learning In 4 Volumes

Handbook Of Financial Econometrics  Mathematics  Statistics  And Machine Learning  In 4 Volumes  Book
Author : Cheng-few Lee,John C Lee
Publisher : World Scientific
Release : 2020-07-30
ISBN : 9811202400
Language : En, Es, Fr & De

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Book Description :

This four-volume handbook covers important concepts and tools used in the fields of financial econometrics, mathematics, statistics, and machine learning. Econometric methods have been applied in asset pricing, corporate finance, international finance, options and futures, risk management, and in stress testing for financial institutions. This handbook discusses a variety of econometric methods, including single equation multiple regression, simultaneous equation regression, and panel data analysis, among others. It also covers statistical distributions, such as the binomial and log normal distributions, in light of their applications to portfolio theory and asset management in addition to their use in research regarding options and futures contracts.In both theory and methodology, we need to rely upon mathematics, which includes linear algebra, geometry, differential equations, Stochastic differential equation (Ito calculus), optimization, constrained optimization, and others. These forms of mathematics have been used to derive capital market line, security market line (capital asset pricing model), option pricing model, portfolio analysis, and others.In recent times, an increased importance has been given to computer technology in financial research. Different computer languages and programming techniques are important tools for empirical research in finance. Hence, simulation, machine learning, big data, and financial payments are explored in this handbook.Led by Distinguished Professor Cheng Few Lee from Rutgers University, this multi-volume work integrates theoretical, methodological, and practical issues based on his years of academic and industry experience.

Frontiers in Fractional Calculus

Frontiers in Fractional Calculus Book
Author : Sachin Bhalekar
Publisher : Bentham Science Publishers
Release : 2018-03-21
ISBN : 1681085992
Language : En, Es, Fr & De

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Book Description :

This book brings together eleven topics on different aspects of fractional calculus in a single volume. It provides readers the basic knowledge of fractional calculus and introduces advanced topics and applications. The information in the book is presented in four parts: Fractional Diffusion Equations: (i) solutions of fractional diffusion equations using wavelet methods, (ii) the maximum principle for time fractional diffusion equations, (iii) nonlinear sub-diffusion equations. Mathematical Analysis: (i) shifted Jacobi polynomials for solving and identifying coupled fractional delay differential equations, (ii) the monotone iteration principle in the theory of Hadamard fractional delay differential equations, (iii) dynamics of fractional order modified Bhalekar-Gejji System, (iv) Grunwald-Letnikov derivatives. Computational Techniques: GPU computing of special mathematical functions used in fractional calculus. Reviews: (i) the popular iterative method NIM, (ii) fractional derivative with non-singular kernels, (iii) some open problems in fractional order nonlinear system This is a useful reference for researchers and graduate level mathematics students seeking knowledge about of fractional calculus and applied mathematics.

Image Processing in MATLAB Roi Processing Image Degradation Color Blocks and Gpu Computing

Image Processing in MATLAB  Roi Processing  Image Degradation  Color  Blocks and Gpu Computing Book
Author : A. Smith
Publisher : Createspace Independent Publishing Platform
Release : 2017-12-30
ISBN : 9781983426674
Language : En, Es, Fr & De

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Book Description :

This book develops a set of reference-standard algorithms and workflow apps for image processing, analysis, visualization, and algorithm development. You can perform image segmentation, image enhancement, noise reduction, geometric transformations, image registration, and 3D image processing. Image Processing Toolbox apps let you automate common image processing workflows. You can interactively segment image data, compare image registration techniques, and batch-process large datasets. Visualization functions and apps let you explore images, 3D volumes, and videos; adjust contrast; create histograms; and manipulate regions of interest (ROIs). Image Processing Toolbox supports four methods to generate a binary mask. The binary mask defines a region of interest (ROI) of the original image. Mask pixel values of 1 indicate the image pixel belongs to the ROI. Mask pixel values of 0 indicate the image pixel is part of the background. Any binary image can be used as a mask, provided that the binary image is the same size as the image being filtered. You can create a mask from a grayscale image by classifying each pixel as belonging to either the region of interest or the background. Filtering a region of interest (ROI) is the process of applying a filter to a region in an image, where a binary mask defines the region. For example, you can apply an intensity adjustment filter to certain regions of an image. The blurring, or degradation, of an image can be caused by many factors: Movement during the image capture process (by the camera or, when long exposure times are used, by the subject), Out-of-focus optics (use of a wide-angle lens, atmospheric turbulence, or a short exposure time, which reduces the number of photons captured) and Scattered light distortion in confocal microscopy Based on this model, the fundamental task of deblurring is to deconvolve the blurred image with the PSF that exactly describes the distortion. The Image Processing Toolbox software provides functions that help you work with color image data. This toolbox supports conversions between members of the CIE family of device-independent color spaces. Certain image processing operations involve processing an image in sections, called blocks or neighborhoods, rather than processing the entire image at once. Several functions in the toolbox, such as linear filtering and morphological functions, use this approach. The toolbox includes several functions that you can use to implement image processing algorithms as a block or neighborhood operation. These functions break the input image into blocks or neighborhoods, call the specified function to process each block or neighborhood, and then reassemble the results into an output image. If you have a Parallel Computing Toolbox license, you can take advantage of multiple processor cores on your machine by specifying the blockproc setting 'UseParallel' as true. The Image Processing Toolbox includes many functions that support the generation of efficient C code using MATLAB Coder. To take advantage of the performance benefits offered by a modern graphics processing unit (GPU), certain Image Processing Toolbox functions have been enabled to perform image processing operations on a GPU. This can provide GPU acceleration for complicated image processing workflows.

Scaling Up Machine Learning

Scaling Up Machine Learning Book
Author : Ron Bekkerman,Mikhail Bilenko,John Langford
Publisher : Cambridge University Press
Release : 2012
ISBN : 0521192242
Language : En, Es, Fr & De

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Book Description :

This integrated collection covers a range of parallelization platforms, concurrent programming frameworks and machine learning settings, with case studies.

Computer Vision in Control Systems 4

Computer Vision in Control Systems 4 Book
Author : Margarita N. Favorskaya,Lakhmi C. Jain
Publisher : Springer
Release : 2017-10-25
ISBN : 3319679945
Language : En, Es, Fr & De

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Book Description :

The research book is a continuation of the authors’ previous works, which are focused on recent advances in computer vision methodologies and technical solutions using conventional and intelligent paradigms. The book gathers selected contributions addressing a number of real-life applications including the identification of handwritten texts, watermarking techniques, simultaneous localization and mapping for mobile robots, motion control systems for mobile robots, analysis of indoor human activity, facial image quality assessment, android device controlling, processing medical images, clinical decision-making and foot progression angle detection. Given the tremendous interest among researchers in the development and applications of computer vision paradigms in the field of business, engineering, medicine, security and aviation, the book offers a timely guide for all PhD students, professors, researchers and software developers working in the areas of digital video processing and computer vision technologies.

Nature Inspired Design of Hybrid Intelligent Systems

Nature Inspired Design of Hybrid Intelligent Systems Book
Author : Patricia Melin,Oscar Castillo,Janusz Kacprzyk
Publisher : Springer
Release : 2016-12-08
ISBN : 331947054X
Language : En, Es, Fr & De

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Book Description :

This book highlights recent advances in the design of hybrid intelligent systems based on nature-inspired optimization and their application in areas such as intelligent control and robotics, pattern recognition, time series prediction, and optimization of complex problems. The book is divided into seven main parts, the first of which addresses theoretical aspects of and new concepts and algorithms based on type-2 and intuitionistic fuzzy logic systems. The second part focuses on neural network theory, and explores the applications of neural networks in diverse areas, such as time series prediction and pattern recognition. The book’s third part presents enhancements to meta-heuristics based on fuzzy logic techniques and describes new nature-inspired optimization algorithms that employ fuzzy dynamic adaptation of parameters, while the fourth part presents diverse applications of nature-inspired optimization algorithms. In turn, the fifth part investigates applications of fuzzy logic in diverse areas, such as time series prediction and pattern recognition. The sixth part examines new optimization algorithms and their applications. Lastly, the seventh part is dedicated to the design and application of different hybrid intelligent systems.

GPU Computing Gems Emerald Edition

GPU Computing Gems Emerald Edition Book
Author : Anonim
Publisher : Elsevier
Release : 2011-01-13
ISBN : 9780123849892
Language : En, Es, Fr & De

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Book Description :

GPU Computing Gems Emerald Edition offers practical techniques in parallel computing using graphics processing units (GPUs) to enhance scientific research. The first volume in Morgan Kaufmann's Applications of GPU Computing Series, this book offers the latest insights and research in computer vision, electronic design automation, and emerging data-intensive applications. It also covers life sciences, medical imaging, ray tracing and rendering, scientific simulation, signal and audio processing, statistical modeling, video and image processing. This book is intended to help those who are facing the challenge of programming systems to effectively use GPUs to achieve efficiency and performance goals. It offers developers a window into diverse application areas, and the opportunity to gain insights from others' algorithm work that they may apply to their own projects. Readers will learn from the leading researchers in parallel programming, who have gathered their solutions and experience in one volume under the guidance of expert area editors. Each chapter is written to be accessible to researchers from other domains, allowing knowledge to cross-pollinate across the GPU spectrum. Many examples leverage NVIDIA's CUDA parallel computing architecture, the most widely-adopted massively parallel programming solution. The insights and ideas as well as practical hands-on skills in the book can be immediately put to use. Computer programmers, software engineers, hardware engineers, and computer science students will find this volume a helpful resource. For useful source codes discussed throughout the book, the editors invite readers to the following website: ..." Covers the breadth of industry from scientific simulation and electronic design automation to audio / video processing, medical imaging, computer vision, and more Many examples leverage NVIDIA's CUDA parallel computing architecture, the most widely-adopted massively parallel programming solution Offers insights and ideas as well as practical "hands-on" skills you can immediately put to use

Computational Science ICCS 2020

Computational Science     ICCS 2020 Book
Author : Valeria V. Krzhizhanovskaya,Gábor Závodszky,Michael H. Lees,Jack J. Dongarra,Peter M. A. Sloot,Sérgio Brissos,João Teixeira
Publisher : Springer Nature
Release : 2020-06-19
ISBN : 3030504360
Language : En, Es, Fr & De

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Book Description :

The seven-volume set LNCS 12137, 12138, 12139, 12140, 12141, 12142, and 12143 constitutes the proceedings of the 20th International Conference on Computational Science, ICCS 2020, held in Amsterdam, The Netherlands, in June 2020.* The total of 101 papers and 248 workshop papers presented in this book set were carefully reviewed and selected from 719 submissions (230 submissions to the main track and 489 submissions to the workshops). The papers were organized in topical sections named: Part I: ICCS Main Track Part II: ICCS Main Track Part III: Advances in High-Performance Computational Earth Sciences: Applications and Frameworks; Agent-Based Simulations, Adaptive Algorithms and Solvers; Applications of Computational Methods in Artificial Intelligence and Machine Learning; Biomedical and Bioinformatics Challenges for Computer Science Part IV: Classifier Learning from Difficult Data; Complex Social Systems through the Lens of Computational Science; Computational Health; Computational Methods for Emerging Problems in (Dis-)Information Analysis Part V: Computational Optimization, Modelling and Simulation; Computational Science in IoT and Smart Systems; Computer Graphics, Image Processing and Artificial Intelligence Part VI: Data Driven Computational Sciences; Machine Learning and Data Assimilation for Dynamical Systems; Meshfree Methods in Computational Sciences; Multiscale Modelling and Simulation; Quantum Computing Workshop Part VII: Simulations of Flow and Transport: Modeling, Algorithms and Computation; Smart Systems: Bringing Together Computer Vision, Sensor Networks and Machine Learning; Software Engineering for Computational Science; Solving Problems with Uncertainties; Teaching Computational Science; UNcErtainty QUantIficatiOn for ComputationAl modeLs *The conference was canceled due to the COVID-19 pandemic. Chapter ‘APE: A Command-Line Tool and API for Automated Workflow Composition’ is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

Deep Learning with MATLAB

Deep Learning with MATLAB Book
Author : A. Vidales
Publisher : Independently Published
Release : 2018-12-30
ISBN : 9781792922176
Language : En, Es, Fr & De

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Book Description :

The treatment of large data requires the use of computational structures that implement parallelism and distributed computing. The Big Data structures are responsible for providing these characteristics to computing. The treatment of large data requires the use of computational structures that implement parallelism and distributed computing. The Big Data structures are responsible for providing these characteristics to computing. You can train a convolutional neural network (CNN, ConvNet) or long short-term memory networks (LSTM or BiLSTM networks) using the trainNetwork function. You can choose the execution environment (CPU, GPU, multi-GPU, and parallel) using trainingOptions. Training in parallel, or on a GPU, requires Parallel Computing Toolbox.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 Deep Learning Toolbox, enables neural network training and simulation to take advantage of each mode of parallelism. 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.Parallel Computing Toolbox allows neural network training and simulation to run acrossmultiple CPU cores on a single PC, or across multiple CPUs on multiple computers on anetwork using MATLAB Distributed Computing Server.Using multiple cores can speed calculations. Using multiple computers can allow you tosolve problems using data sets too big to fi in the RAM of a single computer. The onlylimit to problem size is the total quantity of RAM available across all computers.To manage cluster configurations use the Cluster Profil Manager. You can train a convolutional neural network (CNN, ConvNet) or long short-term memory networks (LSTM or BiLSTM networks) using the trainNetwork function. You can choose the execution environment (CPU, GPU, multi-GPU, and parallel) using trainingOptions. Training in parallel, or on a GPU, requires Parallel Computing Toolbox.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 Deep Learning Toolbox, enables neural network training and simulation to take advantage of each mode of parallelism. 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. Parallel Computing Toolbox allows neural network training and simulation to run acrossmultiple CPU cores on a single PC, or across multiple CPUs on multiple computers on anetwork using MATLAB Distributed Computing Server.Using multiple cores can speed calculations. Using multiple computers can allow you tosolve problems using data sets too big to fi in the RAM of a single computer. The onlylimit to problem size is the total quantity of RAM available across all computers.To manage cluster configurations use the Cluster Profil Manager.

Design of Intelligent Systems Based on Fuzzy Logic Neural Networks and Nature Inspired Optimization

Design of Intelligent Systems Based on Fuzzy Logic  Neural Networks and Nature Inspired Optimization Book
Author : Patricia Melin,Oscar Castillo,Janusz Kacprzyk
Publisher : Springer
Release : 2015-06-12
ISBN : 3319177478
Language : En, Es, Fr & De

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Book Description :

This book presents recent advances on the design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization and their application in areas such as, intelligent control and robotics, pattern recognition, time series prediction and optimization of complex problems. The book is organized in eight main parts, which contain a group of papers around a similar subject. The first part consists of papers with the main theme of theoretical aspects of fuzzy logic, which basically consists of papers that propose new concepts and algorithms based on fuzzy systems. The second part contains papers with the main theme of neural networks theory, which are basically papers dealing with new concepts and algorithms in neural networks. The third part contains papers describing applications of neural networks in diverse areas, such as time series prediction and pattern recognition. The fourth part contains papers describing new nature-inspired optimization algorithms. The fifth part presents diverse applications of nature-inspired optimization algorithms. The sixth part contains papers describing new optimization algorithms. The seventh part contains papers describing applications of fuzzy logic in diverse areas, such as time series prediction and pattern recognition. Finally, the eighth part contains papers that present enhancements to meta-heuristics based on fuzzy logic techniques.