Computational Intelligence: for Engineering and by D. T. Pham, P. T. N. Pham, M. S. Packianather, A. A. Afify

By D. T. Pham, P. T. N. Pham, M. S. Packianather, A. A. Afify (auth.), Diego Andina, Duc Truong Pham (eds.)

Unlike conventional computing, Computational Intelligence is tolerant of obscure info, partial fact and uncertainty. This e-book provides a specific number of contributions on a centred remedy of vital parts of CI, concentrated on its key aspect: studying.

All the individuals of this quantity have direct bearing with this factor. From basics to complex platforms as Multilayer Perceptron synthetic Neural Networks (ANN-MLP), Radial foundation functionality Networks (RBF) and its family members with Fuzzy units and aid Vector Machines idea; and directly to a number of serious functions in Engineering and production. those are between purposes the place CI has first-class strength.

This quantity has specifically taken Neural Networks, key parts of CI, to the subsequent point. either amateur and professional readers can reap the benefits of this well timed addition to CI established literature. in the direction of that target, the editors and the authors have made severe contributions and succeeded. they've got paved the line for studying paradigms in the direction of the answer of many real-world problems.

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This perspective helps to compare Neural Systems with classical Computing Systems and leads to a formal and common presentation that will be used throughout the book INTRODUCTION Computers used nowadays can make a great variety of tasks (whenever they are well defined) at a higher speed and with more reliability than those reached by the human beings. None of us will be, for example, able to solve complex mathematical equations at the speed that a personal computer will. Nevertheless, mental capacity of the human beings is still higher than the one of machines in a wide variety of tasks.

Third Int. Conf. on Genetic Algorithms and Their Applications, George Mason University, 51–61. Schultz G, Fichtner D, Nestler A and Hoffmann J, (1997), “An intelligent tool for determination of cutting values based on neural networks”, Proc. 2nd World Congress on Intelligent Manufacturing Processes and Systems, Budapest, Hungary, 66–71. Seals R C and Whapshott G F, (1994), “Design of HDL programmes for digital systems using genetic algorithms”, AI Eng 9 (ibid), 331–338. Shi Z Z, Zhou H and Wang J, (1997), “Applying case-based reasoning to engine oil design”, Artificial Intelligence in Engineering, 11 (2), 167–172.

Some common crossover operations are one-point crossover, two-point crossover, cycle crossover and uniform crossover. One-point crossover is the simplest crossover operation. Two individuals are randomly selected as parents from the pool of individuals formed by the selection procedure and cut at a randomly selected point. The tails, which are the parts after the cutting point, are swapped and two new individuals (children) are produced. Note that this operation does not change the values of bits.

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