By Yuhu Cheng, Xuesong Wang, Wei Sun (auth.), De-Shuang Huang, Laurent Heutte, Marco Loog (eds.)
The overseas convention on clever Computing (ICIC) used to be shaped to supply an annual discussion board devoted to the rising and hard issues in synthetic intelligence, computer studying, bioinformatics, and computational biology, and so forth. It goals to carry - gether researchers and practitioners from either academia and to proportion rules, difficulties and ideas regarding the multifaceted elements of clever computing. ICIC 2007, held in Qingdao, China, August 21-24, 2007, constituted the 3rd - ternational convention on clever Computing. It equipped upon the luck of ICIC 2006 and ICIC 2005 held in Kunming and Hefei, China, 2006 and 2005, respectively. This 12 months, the convention centred generally at the theories and methodologies in addition to the rising functions of clever computing. Its goal was once to unify the image of up to date clever computing options as an fundamental idea that highlights the traits in complicated computational intelligence and bridges theoretical examine with functions. accordingly, the subject matter for this convention was once “Advanced clever Computing expertise and Applications”. Papers targeting this subject have been solicited, addressing theories, methodologies, and functions in technological know-how and technology.
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Extra resources for Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques: Third International Conference on Intelligent Computing, ICIC 2007, Qingdao, China, August 21-24, 2007. Proceedings
Y. Lin Acknowledgment The authors are grateful for financial support from the National Science Council of Taiwan, the Republic of China, under Grant NSC94-2213-E-150-025-. References 1. Watkins, C. J. C. : Technical Note: Q-Learning, Machine Learning, 8(3-4): (1992)279-292 2. : Q-Learning for Robot. in M. A. Arbib, editor, Handbook of Brain Theory and Neural Networks, (2003) 934-937 3. Parker,L. : Techniques for Learning in Multi-robot Teams, in Robot Teams: From Diversity to Polymorphism (Balch,T and Parker,L.
10) w j < wmin . (11) σ j < σ min . (12) count denotes the cumulative times of a hidden neuron not being activated from the last time of being activated to the current time, C max , wmin , and σ min are where, thresholds of the external migration, attenuation, and atrophy of redundant hidden neurons which should be selected appropriately. It’s noted that when the atrophy phenomenon appears on hidden neurons, the atrophic hidden neurons have no influence on the training samples. But because they lie in the range of sample input, they may have influence on the testing samples, which A Study of Constructive Fuzzy Normalized RBF Neural Networks 7 consequently may weaken the network generalization performance.
All input must be normalized to a range between 0 and 1 when using a uni-polar sigmoid transfer function or between -1 and 1 when using a bi-polar transfer function. This is done to secure the numerical stability of the network. During training, the flowrates of the wells, which are necessary for training, must also be normalized within a range of 0 and 1. During any field development study, the key questions to be addressed are as follows: What are the quantities that we would like to predict?