Advanced Intelligent Computing Theories and Applications. by Yuhu Cheng, Xuesong Wang, Wei Sun (auth.), De-Shuang Huang,

By Yuhu Cheng, Xuesong Wang, Wei Sun (auth.), De-Shuang Huang, Laurent Heutte, Marco Loog (eds.)

The foreign convention on clever Computing (ICIC) used to be shaped to supply an annual discussion board devoted to the rising and difficult themes in man made intelligence, laptop studying, bioinformatics, and computational biology, and so forth. It goals to convey - gether researchers and practitioners from either academia and to proportion principles, difficulties and recommendations concerning the multifaceted points of clever computing. ICIC 2007, held in Qingdao, China, August 21-24, 2007, constituted the 3rd - ternational convention on clever Computing. It outfitted upon the luck of ICIC 2006 and ICIC 2005 held in Kunming and Hefei, China, 2006 and 2005, respectively. This 12 months, the convention focused in most cases at the theories and methodologies in addition to the rising purposes of clever computing. Its target used to be to unify the image of up to date clever computing thoughts as an essential idea that highlights the tendencies in complex computational intelligence and bridges theoretical examine with purposes. consequently, the topic for this convention used to be “Advanced clever Computing know-how and Applications”. Papers targeting this subject matter have been solicited, addressing theories, methodologies, and purposes in technology 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

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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).

If D P, Cj > Tj , input vector P doesn’t belong to any existing clusters, generate the 4. (j + 1)th new neuron and initialize its weight value being Cj+1 = P, and Tj+1 = Tini , and Prj+1 = 1. If D(P,Cj ) < Tj , categorize input vector P being the cluster of the jth neuron, set Prj 5. 6. ( ) = 1, update Cj and Tj according to (1) and (2). Update Pr value of each un-activated along (3). If generated neurons reaching K, compare each neuron’s Prj with a random number rnd() (between 0 and 1), delete one of the neurons when their Pr are larger than rnd().

Until s is terminal or stop condition has been met. Reward r Q learning Q table X0 Xn-1 …………… Cluster …………… X1 Evaluation &update Xn Update Q value State s Fig. 2. 4 Adaptive Resonance Method The ARM architecture modified from FASTis developed in order that provides more suitable clustered results to the Q-learning architecture for achieving the purpose of improving the learning speed and learning stability. The modification involves three parts: normalization, weight update, and pruning mechanism.

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