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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Computational suggestions and methods have continually performed an important position on top of things engineering because the first computer-based keep an eye on platforms have been positioned into operation over two decades in the past. This position has in truth been accelerating over the intervening years because the sophistication of the computing tools and instruments on hand, in addition to the complexity of the keep an eye on difficulties they've been used to resolve, have additionally elevated.
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This publication constitutes the refereed complaints of the 4th overseas Workshop, PMBS 2013 in Denver, CO, united states in November 2013. The 14 papers awarded during this quantity have been conscientiously reviewed and chosen from 37 submissions. the chosen articles commonly hide themes on vastly parallel and high-performance simulations, modeling and simulation, version improvement and research, functionality optimization, strength estimation and optimization, excessive functionality computing, reliability, functionality research, and community simulations.
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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.