Artificial Neural Networks and Machine Learning – ICANN by Marcel A. J. van Gerven, Eric Maris (auth.), Timo Honkela,
By Marcel A. J. van Gerven, Eric Maris (auth.), Timo Honkela, Włodzisław Duch, Mark Girolami, Samuel Kaski (eds.)
This quantity set (LNCS 6791 and LNCS 6792) constitutes the refereed court cases of the 21th foreign convention on synthetic Neural Networks, ICANN 2011, held in Espoo, Finland, in June 2011. The 106 revised complete or poster papers provided have been rigorously reviewed and chosen from quite a few submissions. ICANN 2011 had simple tracks: brain-inspired computing and computer studying study, with robust cross-disciplinary interactions and applications.
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Additional info for Artificial Neural Networks and Machine Learning – ICANN 2011: 21st International Conference on Artificial Neural Networks, Espoo, Finland, June 14-17, 2011, Proceedings, Part II
Including background; this pre-training does not teach the model to distinguish between foreground and background in the training images). For highly structured images the background models were sometimes not suﬃciently powerful so that part of the background was assigned to the foreground even after consolidation of the foreground model. This does not completely prohibit learning of the foreground model but leads to a noisy ﬁnal model. e. 3), which can be incorporated into the Gibbs sampling scheme by modifying equations (3-5).
C Springer-Verlag Berlin Heidelberg 2011 A Distributed Behavioral Model Using Neural Fields 33 results from combination of every agent’s steering behaviors. e. stay close to its neighbors (cohesion), avoid collisions with them (separation) and move in their average direction (alignment) (Fig. 1). The global stimulus is designed by defining the relevance of each behavior relatively to the actual situation. We also consider obstacle avoidance with the highest priority among other stimuli. The control design will be discussed in theoretical terms, supported by simulation results.
5. (c) shows how the contribution of all stimuli provide the appropriate heading directions, which permits to avoid the obstacle. After passing the obstacle the stimulus of obstacle avoidance is removed, and the group continues its movement by combining the three flock behaviors. The global path is illustrated in Fig. 5. (g). 8 0 (a) Phase 1: Cohesion 10 20 30 t [steps] 40 50 60 (b) Phase 1: Headings vs. 5 −1 0 (c) Phase 2: Separation 10 20 30 40 t [steps] 50 60 70 80 (d) Phase 2: Headings vs. 8 0 10 20 30 40 50 60 70 80 t [steps] (e) Phase 3: Alignment (f) Phase 3: Headings vs.