By De-Shuang Huang, Laurent Heutte, Marco Loog
This ebook - along with the 2 volumes LNCS 4681 and LNAI 4682 - constitutes the refereed lawsuits of the 3rd foreign convention on clever Computing, ICIC 2007, held in Qingdao, China in August 2007. The clever computing expertise features a diversity of strategies resembling synthetic intelligence, perceptual and trend attractiveness, evolutionary and adaptive computing, informatics theories and purposes, computational neuroscience and bioscience, gentle computing, case established and limited reasoning, brokers, networking and desktop supported co-operative operating, human laptop interface concerns. ICIC topic unifies the image of up to date clever computing recommendations as an necessary idea that highlights the traits in complex computational intelligence and bridges theoretical learn with functions.
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Extra resources for Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques: Third International Conference ... in Computer and Information Science)
The modification involves three parts: normalization, weight update, and pruning mechanism. 1 Normalization The FAST compares an input vector with each neuron’s center point to calculate the Manhattan distances D. However, the variation of the input vector in its distinct dimension may not have the same scale. Consequently, in the system input space, a slightly changing of a parameter in some dimension of an input vector might make the calculated D larger than T value causing a new neuron being generated.
S. Huang, L. Heutte, and M. ): ICIC 2007, CCIS 2, pp. 11–20, 2007. -P. -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)  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.
Distance − distancei , when n ≠ 1 ; ⎪ . 0 , . when n 1 = ⎩ (5) Where w j represents the proportion that activated neuron j contributes; n denotes the number of activated neurons; ∑ dis tan ce stands to be the summation distance of all activated neurons;distancej is the distance of neuron j. Expected value V is calculated as follows: Vt = max ∑ w jQ j ( s, a) , (6) a where Vt is expected value at time t. Q is updated according to equation (7). 1 Cart-Pole Simulation The well-known cart-pole simulation having been used in verification of the performance of reinforcement learning algorithms is simulated here to check if the proposed architecture works.