Advanced Intelligent Computing Theories and Applications. by De-Shuang Huang, Laurent Heutte, Marco Loog

Computing

By De-Shuang Huang, Laurent Heutte, Marco Loog

This ebook - along with the 2 volumes LNCS 4681 and LNAI 4682 - constitutes the refereed court cases of the 3rd foreign convention on clever Computing, ICIC 2007, held in Qingdao, China in August 2007. The clever computing expertise features a variety of innovations akin to synthetic intelligence, perceptual and trend acceptance, evolutionary and adaptive computing, informatics theories and purposes, computational neuroscience and bioscience, smooth computing, case established and restricted reasoning, brokers, networking and laptop supported co-operative operating, human desktop interface matters. ICIC topic unifies the image of latest clever computing innovations as an crucial idea that highlights the developments in complex computational intelligence and bridges theoretical learn with functions.

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S. -Y. Lin algorithm merges the advantage of variable vigilance value of the ART and the pruning mechanism of the GAR (Grow and Represent Model) [9] theorem, amending the disadvantage of the boxes method which maps only one activated box for each input vector. It dynamically adjusts the size and location of the sensitivity region for activated neurons, and makes boundaries between categories to be changeable, resulting in producing more suitable input for increasing the system learning speed. Furthermore, when an input pattern activates more than one neuron, the pruning mechanism of the FAST appropriately prunes one of the neurons with overlapped sensitivity region for preserving neurons to accommodate more categories in the cause of resource reservation.

Furthermore, when an input pattern activates more than one neuron, the pruning mechanism of the FAST appropriately prunes one of the neurons with overlapped sensitivity region for preserving neurons to accommodate more categories in the cause of resource reservation. The article aims at combining a FAST-based algorithm and Q-learning algorithm into a reinforcement learning algorithm called ARM Q-learning algorithm that improves Q-learning algorithm on learning speed and learning stability. The article is organized in four sections; the first Section being this introduction.

Vol. 97, (1975) 220-227 7. Structure-adaptable Digital Neural Networks, PhD Thesis 2052, Swiss Federal Institute of Technology-Lausanne, Lausanne(1999) 8. :The FAST Architecture: A Neural Network with Flexible Adaptable-size Topology, Proceedings of Fifth International Conference on Microelectronics for Neural Networks, 12-14, (1996) 337–340 9. :Neural Models of Incremental Supervised and Unsupervised Learning. PhD thesis, Swiss Federal Institute of Technology-Lausanne, Lausanne, DPFL, Thesis 863(1990).

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