High Performance Programming for Soft Computing

High Performance Programming for Soft Computing

Oscar Montiel Ross, Roberto Sepulveda Cruz

Language: English

Pages: 371

ISBN: 2:00217421

Format: PDF / Kindle (mobi) / ePub

This book examines the present and future of soft computer techniques. It explains how to use the latest technological tools, such as multicore processors and graphics processing units, to implement highly efficient intelligent system methods using a general purpose computer.

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CPU, is regarded as one of the most important factors in the overall computer performance. Memory which can be classified as volatile and non-volatile, refers to the ability to store data with or without the presence of electrical power. In other words, the content of the volatile memory is not permanent; the stored data is lost when power is removed. In the volatile memory category, we have the Synchronous Dynamic Random-Access Memory (SDRAM), the Dynamic RAM (DRAM), and the Static Random-Access

into four main categories: (1) conversion routines for different sparse matrix formats, (2) routines for operations between a sparse matrix and a set of vectors in dense format, (3) routines for operations between a sparse matrix and a vector in dense format, and (4) functions for operations between a vector in sparse format and a vector in dense format (NVIDIA 2012b). The library takes advantage of the CUDA parallel programming model as well as of the computational resources of the NVIDIA

cutting time, the Cutting Tool Travel Path (CTTP) (J.E.A. Qudeiri 2006) between operations should be minimized. We were working on a continuous travel path, in which the start point and the end point of each operation are the same, and it mainly appears in hole–cutting operations such as drilling, reaming and tapping. Tool Path Opt. Based on ACO for CNC 185 The Travelling Salesman Problem (TSP) and Parallel Ant Colony Optimization (P-ACO) have been incorporated to find the shortest cutting tool

type-2, so it is necessary for the extended defuzzification operation to get T1FS at the output. Since this operation converts type-2 output sets to a type-1 fuzzy set, it is called type reduction, and the T1FS obtained is called a type-reduced set. The type-reduced fuzzy set may then be defuzzified to obtain a single crisp number. 1.5.1 Interval Type-2 Fuzziϔication Stage The task of the fuzzifier is to map a crisp value into a T2FS in . For each singleton input, there are two membership grades

neuron. Here McCulloch and Pitts in 1943 introduced the first neural network computing model. In 1949, Hebb’s book entitled The Organization of Behavior presented for the first time the physiological learning rule for synaptic modification. This book was an important source of inspiration that contributed to the development of new computational models of learning and adaptive systems. Many other important works were published and it is impossible to cite all of them here; however, some works that

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