Artificial Intelligence and Soft Computing – ICAISC 2006: - download pdf or read online

By Leon Bobrowski (auth.), Leszek Rutkowski, Ryszard Tadeusiewicz, Lotfi A. Zadeh, Jacek M. Żurada (eds.)

ISBN-10: 3540357483

ISBN-13: 9783540357483

This e-book constitutes the refereed court cases of the eighth overseas convention on man made Intelligence and smooth Computing, ICAISC 2006, held in Zakopane, Poland, in June 2006.

The 128 revised contributed papers provided have been conscientiously reviewed and chosen from four hundred submissions. The papers are equipped in topical sections on neural networks and their purposes, fuzzy platforms and their purposes, evolutionary algorithms and their purposes, tough units, type and clustering, snapshot research and robotics, bioinformatics and scientific functions, a number of difficulties of synthetic intelligence.

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Extra info for Artificial Intelligence and Soft Computing – ICAISC 2006: 8th International Conference, Zakopane, Poland, June 25-29, 2006. Proceedings

Sample text

The second tasks comprises the reading data from the record describing the required i-th segment and executing the calculations according to (2). The search for the proper segment is realized by a method similar to the successive approximation used in AD converters. It has been assumed that the total number of segments is an integer power of 2. Individual bits of the required i value are determined in succession, starting from the MSB bit, in consecutive clock cycles. The whole procedure looks as follows.

2. -M. C. -H. Lee Fig. 3. KLMSBLDM(training) Fig. 4. KLMSBL(training) Fig. 5. KLMSBLDM(testing) Fig. 6. 4 shows KLMSBL which does not apply dynamic momentum. 3, although the values of both RMSE, which does not include a bias term, and RMSEB, which includes a bias term within learning weight vectors, are similar, RMS errors in the method using matrix inverse with regularization parameter are mostly lower than those of other methods. 4, RMS errors in RMSE and RMSEB are similar each other, but compared with these values, InvRMSE and InvRMSEB have large values.

L-Curve(Iris) Fig. 2. -M. C. -H. Lee Fig. 3. KLMSBLDM(training) Fig. 4. KLMSBL(training) Fig. 5. KLMSBLDM(testing) Fig. 6. 4 shows KLMSBL which does not apply dynamic momentum. 3, although the values of both RMSE, which does not include a bias term, and RMSEB, which includes a bias term within learning weight vectors, are similar, RMS errors in the method using matrix inverse with regularization parameter are mostly lower than those of other methods. 4, RMS errors in RMSE and RMSEB are similar each other, but compared with these values, InvRMSE and InvRMSEB have large values.

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Artificial Intelligence and Soft Computing – ICAISC 2006: 8th International Conference, Zakopane, Poland, June 25-29, 2006. Proceedings by Leon Bobrowski (auth.), Leszek Rutkowski, Ryszard Tadeusiewicz, Lotfi A. Zadeh, Jacek M. Żurada (eds.)


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