Download E-books Advances in Computational Intelligence: 12th International Work-Conference on Artificial Neural Networks, IWANN 2013, Proceedings, Part 1 PDF

By Ignacio Rojas, Gonzalo Joya, Joan Cabestany

This two-volume set LNCS 7902 and 7903 constitutes the refereed complaints of the twelfth overseas Work-Conference on man made Neural Networks, IWANN 2013, held in Puerto de los angeles Cruz, Tenerife, Spain, in June 2013. The 116 revised papers have been conscientiously reviewed and chosen from a variety of submissions for presentation in volumes. The papers discover sections on mathematical and theoretical tools in computational intelligence, neurocomputational formulations, studying and version emulation of cognitive capabilities, bio-inspired platforms and neuro-engineering, complicated subject matters in computational intelligence and functions

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1e-1 nine. 0e-2 eight. 8e-1 2. 1e-1 four. 4e-1 five. 0e-2 three. 4e+1 nine. 35 nine. 8e-1 1. 1e-1 eight. 4e-1 five. 8e-2 1. 2e+1 2. 6e-1 three. 4e+1 three. 1e+1 2. 2e+1 eight. eight 1. 1e+1 three. five 1. 2e+2 2. 1e+1 1. 9e+1 2. nine 1. 9e+1 four. four 2. 1e+1 6. nine 32 A. Lendasse et al. TROP-ELM plays on common 27% larger than the unique OP-ELM and offers a customary deviation of the consequences fifty two% under that of the OP-ELM (also on regular over the 10 facts sets). additionally, the TROP-ELM is obviously pretty much as good (or greater) because the GP in six out of the 10 info units —Ailerons, Elevators, automobile rate, financial institution and Boston— within which situations it has an analogous (or reduce) general deviation of the implications. This with a computational time often or 3 orders of value below the GP. desk three supplies the computational occasions for every set of rules and every facts set (average of the 10 repetitions). desk three. Computational occasions (in seconds) for all 5 methodologies at the regression info units. “Auto P. ” stands for automobile rate dataset. SVM MLP GP ELM OP-ELM TROP-ELM MLE-ELM Abalone Ailerons Elevators computing device automobile P. CPU Servo 6. 6e+4 2. 1e+3 nine. 5e+2 four. 0e-1 five. 7 12. 2 20 1. 3e+2 five. 2e+2 2. 2 three. 9e-2 2. 1e-1 eight. 4e-1 2. 6e-1 four. 2e+2 three. 5e+3 2. 9e+3 nine. 0e-1 sixteen. eight 14. 6 35 five. 8e+2 three. 5e+3 6. 5e+3 1. 6 29. eight forty four. three fifty one three. 2e+5 eight. 2e+3 6. 3e+3 1. 2 26. 2 thirteen. nine forty three 2. 6e+2 7. 3e+2 2. nine three. 8e-2 2. 7e-1 four. 8e-1 2. 7e-1 three. 2e+2 five. 8e+2 three. 2 four. 2e-2 2. 0e-1 1. 2 1. 6e-1 financial institution shares Boston 1. 6e+3 2. 7e+3 1. 7e+3 four. 7e-1 eight. 03 four. four 23 2. 3e+3 1. 2e+3 four. 1e+1 1. 1e-1 1. fifty four 1. 1 thirteen eight. 5e+2 eight. 2e+2 eight. five 7. 4e-2 7. 0e-1 1. five 2. nine it may be visible that the TROP-ELM retains computational instances of an identical order as that of the OP-ELM (although greater on average), and continues to be numerous orders of magnitudes quicker than the GP, MLP or SVM. in fact, as for the OP-ELM, the computational occasions stay one to 2 orders of importance above the unique ELM. the consequences bought with the MLE-ELM are greater than with the TROP-ELM for three datasets and comparable for four different datasets. For the three datasets for which the MLE-ELM isn't pretty much as good as TROP-ELM, the performances are besides higher than with SVM or MLP. The computational time of the MLE-ELM is normally higher (2 or three times slower); however it could be spotted that the MLE-ELM was once no longer parallelized for those experiments. in truth, the MLE-ELM can intrinsically be parallelized and the computational time should be approximatively divided by means of the variety of on hand cores. a couple of cores equivalent to the variety of types which are assembled is perhaps optimum. 7 Sensitivity to Variable choice: an easy try during this part, an easy try to make sure and try out the robustness of ELM strategies is brought. The abalone facts set is used and in an effort to upload artificially a few inappropriate yet based variables, a subset of the aileron dataset is concatenated to the abalone dataset. the recent dataset has then an analogous variety of samples than the unique dataset however the variety of variables is now thirteen rather than eight. evidently, the hot five variables can't support development any regression version. additionally, those additional variables may possibly pollute the hidden neurons of the ELM options in view that they're bringing info by way of the random projection.

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