Download Artificial Neural Networks and Machine Learning – ICANN by Botond Attila Bócsi, Lehel Csató (auth.), Valeri Mladenov, PDF

By Botond Attila Bócsi, Lehel Csató (auth.), Valeri Mladenov, Petia Koprinkova-Hristova, Günther Palm, Alessandro E. P. Villa, Bruno Appollini, Nikola Kasabov (eds.)

The booklet constitutes the lawsuits of the twenty third overseas convention on synthetic Neural Networks, ICANN 2013, held in Sofia, Bulgaria, in September 2013. The seventy eight papers incorporated within the court cases have been rigorously reviewed and chosen from 128 submissions. the point of interest of the papers is on following themes: neurofinance graphical community versions, mind laptop interfaces, evolutionary neural networks, neurodynamics, advanced structures, neuroinformatics, neuroengineering, hybrid platforms, computational biology, neural undefined, bioinspired embedded platforms, and collective intelligence.

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Extra resources for Artificial Neural Networks and Machine Learning – ICANN 2013: 23rd International Conference on Artificial Neural Networks Sofia, Bulgaria, September 10-13, 2013. Proceedings

Sample text

Most of the research work provides experimental results with values of h ranging from 2 to 20. We found that lower values of h have a negative effect on the classification accuracy, whereas higher values give time series that cannot be efficiently indexed by multi-dimensional indexing methods. In our future work, we plan to further investigate the effect the dimensionality of time series has on the performance of classification. All experiments were run twice, once on the original time series and once on their 12-dimensional representations.

The proposed algorithm has been run on the resulting time series described by figure 4(b) by setting K = 2. The clustering results are displayed figure on 4(c). 540 540 True data Class 1 Class 2 True data 1500 1000 500 1000 500 500 480 480 460 460 t 520 440 X t 2000 X Power (Watt) 2500 520 440 420 420 400 400 380 380 800 0 0 600 1 2 400 3 Sampling time (second) 200 4 5 6 0 360 360 Curve number 340 0 100 200 300 400 500 Time 600 700 800 900 340 0 100 200 300 400 500 600 700 800 900 Time Fig. 4. (left) Examples of signals acquired during successive switch operations; (middle) Real data without the trajectories; (right) Clustering results obtained with EM-ML for the real data set.

4 Discussion In summary, we have developed analytical alternatives to evaluate and approximate solutions of delay differential equations that can be used for delay-coupled reservoir computing. It is shown that the resulting update equations in principle lose neither accuracy with respect to the system dynamics nor computational power in DCR benchmark tasks. Using the analytical approximation reduces An Analytical Approach to Delay-Coupled Reservoir Computing 33 computational costs considerably. This enabled us to study larger networks of delay-coupled nodes, yielding a dramatic increase in nonlinear benchmark performance.

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