Download Tree-Structure based Hybrid Computational Intelligence: by Yuehui Chen PDF

By Yuehui Chen

Research in computational intelligence is directed towards development pondering machines and bettering our knowing of intelligence. As obtrusive, the last word fulfillment during this box will be to imitate or exceed human cognitive services together with reasoning, acceptance, creativity, feelings, realizing, studying and so forth. during this booklet, the authors illustrate an hybrid computational intelligence framework and it purposes for numerous challenge fixing initiatives. according to tree-structure dependent encoding and the explicit functionality operators, the types should be flexibly built and developed by utilizing easy computational intelligence suggestions. the most thought at the back of this version is the versatile neural tree, that is very adaptive, actual and effective. in keeping with the pre-defined instruction/operator units, a versatile neural tree version should be created and evolved.

This quantity includes of 6 chapters together with an introductory bankruptcy giving the basic definitions and the final bankruptcy presents a few very important study demanding situations. teachers, scientists in addition to engineers engaged in examine, improvement and alertness of computational intelligence thoughts and information mining will locate the excellent insurance of this e-book invaluable.

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13. 14. Initialize the size of the particle swarm n, and other parameters. Initialize the positions and the velocities for all the particles randomly. 19); Next j Next i End While. The end criteria are usually one of the following: • Maximum number of iterations: the optimization process is terminated after a fixed number of iterations, for example, 1000 iterations. • Number of iterations without improvement: the optimization process is terminated after some fixed number of iterations without any improvement.

6) j=1 where P is the total number of samples, y1j and y2j are the actual time-series and the flexible neural tree model output of j-th sample. F it(i) denotes the fitness value of i-th individual. , genetic algorithms (GA), evolution strategy (ES), evolutionary programming (EP), particle swarm optimization (PSO), estimation of distribution algorithm (EDA), and so on. In order to learn the structure and parameters of a flexible neural tree simultaneously, a tradeoff between the structure optimization and parameter learning should be taken.

1, in which the fitness function is calculated by mean square error (MSE) or root mean square error(RMSE) 03. 2. In this stage, the tree structure or architecture of flexible neural tree model is fixed, and it is the best tree taken from the end of run of the PIPE search. 5 2 y = (1 + x−2 ) , 1 + x2 1 ≤ x1 , x2 ≤ 5. 7) 50 training and 200 test samples are randomly generated within the interval [1, 5]. The static nonlinear function is approximated by using the neural tree model with the pre-defined instruction sets I = {+2 , +3 , .

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