By Stefan Wermter, Cornelius Weber, Włodzisław Duch, Timo Honkela, Petia Koprinkova-Hristova, Sven Magg, Günther Palm, Alessandro E. P. Villa (eds.)
The booklet constitutes the lawsuits of the twenty fourth foreign convention on synthetic Neural Networks, ICANN 2014, held in Hamburg, Germany, in September 2014.
The 107 papers integrated within the lawsuits have been rigorously reviewed and chosen from 173 submissions. the focal point of the papers is on following issues: recurrent networks; aggressive studying and self-organisation; clustering and class; timber and graphs; human-machine interplay; deep networks; conception; reinforcement studying and motion; imaginative and prescient; supervised studying; dynamical types and time sequence; neuroscience; and applications.
Read or Download Artificial Neural Networks and Machine Learning – ICANN 2014: 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings PDF
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Additional resources for Artificial Neural Networks and Machine Learning – ICANN 2014: 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings
Blue line is robot trajectory during on-line control and learning. 2 0 0 100 200 300 400 500 time Fig. 3. ESN predictions (J) in comparison with utility function (U) during the all training course in parallel with time varying parameter γ 30 P. Koprinkova-Hristova J U 6 J, U 5 4 3 2 1 0 -1 -2 -3 -4 0 500 1000 time 1500 Fig. 4. 5 -2 0 500 1000 time 1500 2000 2500 Fig. 5. Predictions of ESN critic trained with IP tuning of reservoir Next we investigated the predictions of ESN critic in comparison with utility function in the case when IP tuning is not involved (Fig.
In the present study, ﬁrst we demonstrate the learning results for a conventional CTRNN in which the abovementioned 12 ﬂuctuating Lissajous curves were reused as training data. Note that the learning results for an S-CTRNN have already been published in . This study revisits the results for S-CTRNN and compares them with the learning results for CTRNN in order to clarify the essential diﬀerences in learning capabilities between conventional CTRNN and S-CTRNN. In addition, the present paper presents the results of an additional experiment on the recognition of ﬂuctuating temporal patterns using the trained S-CTRNN in order to demonstrate the recognition capabilities of the network, and provides an analysis of a self-organized initial state space of the S-CTRNN that seems to contribute to both its learning and recognition capabilities.
In (b), untrained attractors appeared (such as (1, 2)), and in (c), almost none of the training patterns were learned. (1) (5) (9) × × × Output of conventional CTRNN (2) (6) × × × (10) (a) × (4) (1) (7) (8) (5) (11) (12) (9) (3) × × × Output of conventional CTRNN (2) × (6) × (3) (4) (1) (7) (8) (5) (12) (9) × (10) (11) (b) × × × Output of conventional CTRNN (2) (6) × (3) × (7) (4) × × × (10) (11) (8) × × × (12) (c) Fig. 2. Phase plots of output generated by CTRNNs initialized with diﬀerent random values.