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

By De-Shuang Huang, Laurent Heutte, Marco Loog

This booklet - together with the 2 volumes LNCS 4681 and LNAI 4682 - constitutes the refereed complaints of the 3rd overseas convention on clever Computing, ICIC 2007, held in Qingdao, China in August 2007. The clever computing expertise incorporates a variety of innovations similar to synthetic intelligence, perceptual and development acceptance, evolutionary and adaptive computing, informatics theories and functions, computational neuroscience and bioscience, gentle computing, case dependent and restricted reasoning, brokers, networking and machine supported co-operative operating, human computing device interface matters. ICIC topic unifies the image of latest clever computing innovations as an indispensable idea that highlights the tendencies in complicated computational intelligence and bridges theoretical examine with functions.

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Figure 8 compares production profile of field sector between two different conditions, with field development scheme and a production scenario without new well. Result demonstrates to need new drilling well in special field sector. Oil Field Development Using Neural Network Model 37 Oil Production Rate of Sector(STB/Day) 2400 without new well 2200 field development scheme, NS-ANN 2000 1800 1600 1400 1200 1000 0 365 730 1095 1460 1825 Time(Days) Fig. 8. Effect of new well on production profile of sector field obtained by NS-ANN model Acknowledgments The authors would like to acknowledge the financial and scientific support from the Islamic Azad University, Karaj branch (KIAU).

2. Initialize the system. Present an input pattern P and compute D(P, Cj) for every operational neuron j. Where D(P, Cj) is the Manhattan distance, a distance between two points measured An ARM-Based Q-Learning Algorithm 3. 4. , on a plane with two points, p1 at (x1, y1) and p2 at (x2, y2), their Manhattan distance is |x1 - x2| + |y1 - y2|. If D(P, Cj) > Tj for all j, activate a new neuron j* by initializing its reference vector to Cj* = P, its threshold to Tj* = Tini, and its deactivation parameter to Prj* = 1.

This leads us to modify the pruning mechanism that keeps neurons that are having high probability of being activated, deletes neurons which have low probability of being re-activated. The pruning mechanism is modified to be: when a neuron is activated, its Pr value is set to be 1, while the Pr values of other un-activated neurons are updated basing on (3). The neuron number is limited to be K. When the number of generated neurons of the system reaches K, the pruning mechanism is triggered to check the Pr values of each neuron.

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