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

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

Ignacio Rojas, Gonzalo Joya, Joan Cabestany

Language: English

Pages: 686

ISBN: 2:00206115

Format: PDF / Kindle (mobi) / ePub


This two-volume set LNCS 7902 and 7903 constitutes the refereed proceedings of the 12th International Work-Conference on Artificial Neural Networks, IWANN 2013, held in Puerto de la Cruz, Tenerife, Spain, in June 2013. The 116 revised papers were carefully reviewed and selected from numerous submissions for presentation in two volumes. The papers explore sections on mathematical and theoretical methods in computational intelligence, neurocomputational formulations, learning and adaptation emulation of cognitive functions, bio-inspired systems and neuro-engineering, advanced topics in computational intelligence and applications

The Intelligent Web: Search, Smart Algorithms, and Big Data

Multi-Agent Machine Learning: A Reinforcement Approach

Mastering Cloud Computing: Foundations and Applications Programming

Computing with Spatial Trajectories

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Cient´ıfico-Tecnol´ ogico, planta 3, C. Gonzalo Guti´errez Quir´ os s/n, 33600 Mieres (Asturias), Spain {oscar.cordon,krzysztof.trawinski}@softcomputing.es Dept. of Computer Science and Artificial Intelligence (DECSAI) and the Research Center on Information and Communication Technologies (CITIC-UGR), University of Granada, 18071 Granada, Spain ocordon@decsai.ugr.es Abstract. Fuzzy rule-based systems have shown a high capability of knowledge extraction and representation when modeling complex,

letter 0.0760 0.0658 0.2926 magic 0.1304 0.1268 0.2366 marketing 0.6690 0.6745 0.6875 mfeat fac 0.0461 0.0501 0.0655 mfeat fou 0.1924 0.1948 0.2205 mfeat kar 0.0737 0.0867 0.0597 mfeat zer 0.2220 0.2294 0.2473 musk2 0.0321 0.0283 0.1121 optdigits 0.0289 0.0297 0.0717 pblocks 0.0341 0.0330 0.0705 pendigits 0.0136 0.0161 0.0861 ring norm 0.0326 0.0397 0.0202 sat 0.1007 0.0967 0.1731 segment 0.0296 0.0326 0.1198 sensor read 24 0.0231 0.0232 0.3703 shuttle 0.0009 0.0009 0.0157 spambase 0.0640 0.0658

Interpretability Issues in Fuzzy Modeling. Springer, Heidelberg (2003) 9. Alonso, J.M., Magdalena, L., Gonz´ alez-Rodr´ıguez, G.: Looking for a good fuzzy system interpretability index: An experimental approach. International Journal of Approximate Reasoning 51, 115–134 (2009) 10. Dasarathy, B.V., Sheela, B.V.: A composite classifier system design: Concepts and methodology. Proceedings of IEEE 67(5), 708–713 (1979) 11. Breiman, L.: Bagging predictors. Machine Learning 24(2), 123–140 (1996) 12.

. Emiro de la Hoz Franco, Andr´es Ortiz Garc´ıa, Julio Ortega Lopera, Eduardo de la Hoz Correa, and Alberto Prieto Espinosa 530 XX Table of Contents – Part I Advances in Computational Intelligence A Novel Neural Network Parallel Adder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fangyue Chen, Guangyi Wang, Guanrong Chen, and Qinbin He 538 Improved Swap Heuristic for the Multiple Knapsack Problem . . . . . . . . . . Yacine Laalaoui 547 Maximum Margin Clustering for State

output adaptive threshold After the synthesis of the GMDH model according to the methodology presented in Sect. 2, it is possible to employ it for robust fault detection. The detection of the faulty sensor for the temperature t1 (simulated during 10sec.) via output adaptive threshold and the faulty first electric heater of the tunnel furnace via input adaptive threshold are presented in Fig. 5 and Fig. 6, respectively. 104 M. Witczak, M. Mrugalski, and J. Korbicz Voltage u1 and input adaptive

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