Advances in Neural Networks – ISNN 2012: 9th International - download pdf or read online

By Dazhong Ma, Jinhai Liu, Zhanshan Wang (auth.), Jun Wang, Gary G. Yen, Marios M. Polycarpou (eds.)

ISBN-10: 3642313612

ISBN-13: 9783642313615

ISBN-10: 3642313620

ISBN-13: 9783642313622

The two-volume set LNCS 7367 and 7368 constitutes the refereed complaints of the ninth foreign Symposium on Neural Networks, ISNN 2012, held in Shenyang, China, in July 2012. The 147 revised complete papers offered have been rigorously reviewed and chosen from a number of submissions. The contributions are based in topical sections on mathematical modeling; neurodynamics; cognitive neuroscience; studying algorithms; optimization; development popularity; imaginative and prescient; picture processing; info processing; neurocontrol; and novel applications.

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Read Online or Download Advances in Neural Networks – ISNN 2012: 9th International Symposium on Neural Networks, Shenyang, China, July 11-14, 2012. Proceedings, Part II PDF

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Additional info for Advances in Neural Networks – ISNN 2012: 9th International Symposium on Neural Networks, Shenyang, China, July 11-14, 2012. Proceedings, Part II

Example text

Third, our proposed method was applied. 0, were supplied to construct the initial population of chromosomes (marked as MF RW KNN). Table 3 show the results of comparable experiments on data sets from UCI in detail. The accuracy values shown in the table are the average value of the 10 runs. In the column of measure function ’F’, the value written in the table is the abbreviation of the measure function and the value in brackets is the number of occurrences in the 10 runs. ’ is the mean number of selected features according to the measure function which occurred most frequently in the 10 runs.

05 to 20. We make 200 trials and Table 2 shows the results of simulation. From Table 2, we can see the performance of the FMNN with new algorithm for pattern classification is better than FMCN and GFMN and is almost the same with DCFMN. Example to Testify the Speed of Pattern Classification We use the same work condition to test the speed of four different methods. The training data and testing data are all Iris data set. The command orders ‘tic’ and ‘toc’ of Matlab were used to record the time of computation.

A fuzzy neural network for pattern classification and feature selection. Fuzzy Sets and Systems 130(1), 101–108 (2002) 6. : A Self-Evolving Interval Type-2 Fuzzy Neural Network With Online Structure and Parameter Learning. IEEE Trans. Fuzzy Systems 16(6), 1411–1424 (2008) The Pattern Classification Based on FMNN with New Algorithm 9 7. : Fuzzy Min-Max neural networks: from classification to regression. Soft Computation 5(6), 69–76 (2001) 8. : Fuzzy Min-Max neural networks-PartI: Classification. IEEE Trans.

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Advances in Neural Networks – ISNN 2012: 9th International Symposium on Neural Networks, Shenyang, China, July 11-14, 2012. Proceedings, Part II by Dazhong Ma, Jinhai Liu, Zhanshan Wang (auth.), Jun Wang, Gary G. Yen, Marios M. Polycarpou (eds.)


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