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Download Advances of Computational Intelligence in Industrial Systems by Ying Liu, Aixin Sun, Han Tong Loh, Wen Feng Lu, Ee-Peng Lim PDF

By Ying Liu, Aixin Sun, Han Tong Loh, Wen Feng Lu, Ee-Peng Lim

ISBN-10: 3540782966

ISBN-13: 9783540782964

Computational Intelligence (CI) has emerged as a fast becoming box over the last decade. Its quite a few suggestions were famous as strong instruments for clever info processing, determination making and data administration.

''Advances of Computational Intelligence in commercial Systems'' reviews the exploration of CI frontiers with an emphasis on a huge spectrum of real-world functions. part I thought and origin provides a number of the newest advancements in CI, e.g. particle swarm optimization, net prone, info mining with privateness safety, kernel tools for textual content research, and so on. part II commercial program covers the CI purposes in a large choice of domain names, e.g. scientific selection help, technique tracking for commercial CNC laptop, novelty detection for jet engines, ant set of rules for berth allocation, and so on.

Such a set of chapters has offered the cutting-edge of CI functions in and should be a necessary source for execs and researchers who desire to study and notice the possibilities in making use of CI recommendations to their specific difficulties.

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Additional resources for Advances of Computational Intelligence in Industrial Systems

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To judge the accuracy of the algorithms, we run all of them for a long duration upto 100,000 FEs. Each algorithm is run independently (with a different seed for the random number generator in every run) for 30 times and the mean best J value obtained along with the standard deviations have been repored for the design problem (32) in Table 5. Figures 14 and 15 illustrate the frequency responses of the filters. The notation Jb has been used to denote four sets of experiments performed with the value of J obtained using exponent b = 1, 2, 4 and 8.

9) where f () is the function to be minimized. So if the new trial vector yields a better value of the fitness function, it replaces its target in the next generation; otherwise the target vector is retained in the population. t. the fitness function) or remains constant but never deteriorates. The DE/rand/1 algorithm is outlined below: Procedure DE Input: Randomly initialized position and velocity of the particles: xi (0) Output: Position of the approximate global optima X ∗ Begin Initialize population; Evaluate fitness; For i = 0 to max-iteration do Begin Create Difference-Offspring; Evaluate fitness; If an offspring is better than its parent Then replace the parent by offspring in the next generation; End If; End For; End.

Different maximum generations (Gmax ) were used according to the complexity of the problem. 001. 998. Table 3 compares the algorithms on the quality of the best solution. The mean and the standard deviation (within parentheses) of the best-of-run solution for 50 independent runs of each of the six algorithms are presented in Table 3. Missing values of standard deviation in this table indicate a zero standard deviation. The best solution in each case has been shown in bold. Table 4 shows results of unpaired t-tests between the best algorithm and the second best in each case (standard error of difference of the two means, 95% confidence interval of this difference, the t value, and the two-tailed P value).

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