Read e-book online Engineering Applications of Neural Networks: 15th PDF

Read e-book online Engineering Applications of Neural Networks: 15th PDF

By Valeri Mladenov, Chrisina Jayne, Lazaros Iliadis

This quantity constitutes the refereed court cases of the fifteenth overseas convention on Engineering purposes of Neural Networks, EANN 2014, held in Sofia, Bulgaria, in September 2014. The 18 revised complete papers awarded including five brief papers have been rigorously reviewed and chosen from 37 submissions. The papers exhibit various functions of neural networks and different computational intelligence techniques to difficult difficulties correct to society and the economic system. those contain components comparable to: environmental engineering, facial features reputation, class with parallelization algorithms, keep watch over of self sufficient unmanned aerial automobiles, clever shipping, flood forecasting, class of scientific photos, renewable power structures, intrusion detection, fault category and basic engineering.

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Read Online or Download Engineering Applications of Neural Networks: 15th International Conference, EANN 2014, Sofia, Bulgaria, September 5-7, 2014. Proceedings PDF

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Additional info for Engineering Applications of Neural Networks: 15th International Conference, EANN 2014, Sofia, Bulgaria, September 5-7, 2014. Proceedings

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2. Compute the percentage of samples in which each pair i, j is correlated. 3. Set Rij = 1, if the corresponding feature pair i, j, with i = j, is correlated on more than θP % samples. 4. e. Rii = 0 for all features i, to discount trivial correlations. The redundancy matrix provides a unified picture of which variables are mutually correlated on a sufficiently large share of input samples. Experimentally, we have determined that a value of θP % = 20% is already sufficient to detect redundancies in a variety of experimental scenarios (nevertheless the value can also be determined on a per-task basis though cross-validation).

101–106 (2004) 10. : Quaternionic Multilayer Perceptrons for Chaotic Time Series Prediction. IEICE Transactions on Fundamentals E79-A(10), 1682–1688 (1996) 11. : Quaternion-Valued Nonlinear Adaptive Filtering. IEEE Transactions on Neural Networks 22(8), 1193–1206 (2011) 12. : Quaternion Neural Network Inversion for Solving Inverse Problems. In: Proceedings of SICE Annual Conference 2012, pp. 1802–1805 (2012) 13. : Remarks on Quaternion Neural Networks with Application to Robot Control. In: Proceedings of SICE 2013 International Conference on Instrumentation, Control, Information Technology and System Integration, pp.

E. Ri· contains only zeros) (a) Add i to the selected subset: SF = SF ∪ {i}; (b) Remove i from F and remove the corresponding entries in R; (c) If an uncorrelated feature j is generated as result of the previous step, move j from F to DF and remove the corresponding entries in R. 2. RULE 1 - If a row Ri· is correlated with all the others and R is not a matrix of all ones: (a) Add i to the deleted subset: DF = DF ∪ {i}; (b) Remove i from F and remove the corresponding entries in R. 3. e. R contains only ones, (a) Select the feature i that is less correlated with those currently in SF ; (b) Add i to the selected subset: SF = SF ∪ {i}; (c) Remove i from F ; (d) Move the remaining features F to the deleted subset (DF = DF ∪ F ) and terminate.

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