By Abdelhamid Bouchachia (eds.)
This booklet constitutes the lawsuits of the overseas convention on Adaptive and clever structures, ICAIS 2014, held in Bournemouth, united kingdom, in September 2014. the nineteen complete papers integrated in those court cases including the abstracts of four invited talks, have been rigorously reviewed and chosen from 32 submissions. The contributions are prepared below the subsequent topical sections: advances in function choice; clustering and type; adaptive optimization; advances in time sequence analysis.
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Additional info for Adaptive and Intelligent Systems: Third International Conference, ICAIS 2014, Bournemouth, UK, September 8-10, 2014. Proceedings
XDj ) be jth instance modiﬁed at ith position. For each data instance j, partial sensitivity is deﬁned by Sij = (f (xij ) − yi )2 − (f (xj ) − yi ))2 , (1) where f is the neural network function and yi is the target value for ith data instance. Further, the sensitivity of the network to variable i is deﬁned as: Si = j Sij N (2) In our implementation of SBP, the algorithm starts with the full set of features (D = 20). At each step, a target neural network is trained. Further, its sensitivity is computed for particular inputs and the feature, for which the sensitivity is smallest is removed from the data.
An introduction to hidden Markov models. IEEE ASSP Magazine 3(1), 4–16 (1986) 17. : Texture classification by means of hmm modeling of am-fm features. In: Proc. ICIP, pp. 182–185 (2001) 18. : The truth of the f-measure. School of Computer Science. University of Manchester (2007) 19. : A statistical approach to material classification using image patch exemplars. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(11), 2032– 2047 (2009) 20. : Color texture analysis using the wavelet-based hidden Markov model.
Table 1. The description of features in the original set Number Feature 1 Air temperature set point in active zone 1 [◦ C] .. 4 Neural Networks In the underlying experiments, the two simple models were used. The ﬁrst one is the feed-forward neural network with one hidden layer trained by LevenbergMarquardt algorithm. This is one of the most popular methods used in neural network applications. The second network is the recurrent neural network with one hidden layer whose delayed outputs are connected back to the input .