By Yongquan Zhou, Yanlian Du, Zhengxin Huang (auth.), De-Shuang Huang, Martin McGinnity, Laurent Heutte, Xiao-Ping Zhang (eds.)
The overseas convention on clever Computing (ICIC) was once shaped to supply an annual discussion board devoted to the rising and demanding issues in man made intel- gence, computing device studying, development popularity, photo processing, bioinformatics, and computational biology. It goals to collect researchers and practitioners from either academia and to proportion principles, difficulties, and ideas with regards to the m- tifaceted points of clever computing. ICIC 2010, held in Changsha, China, August 18-21, 2010, constituted the sixth - ternational convention on clever Computing. It equipped upon the good fortune of ICIC 2009, ICIC 2008, ICIC 2007, ICIC 2006, and ICIC 2005, that have been held in Ulsan, Korea, Shanghai, Qingdao, Kunming and Hefei, China, respectively. This 12 months, the convention focused as a rule at the theories and methodologies in addition to the rising purposes of clever computing. Its goal was once to unify the image of up to date clever computing thoughts as an indispensable idea that highlights the traits in complicated computational intelligence and bridges theoretical study with functions. accordingly, the topic for this convention used to be “Advanced clever Computing expertise and Applications.” Papers targeting this subject 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: 6th International Conference on Intelligent Computing, ICIC 2010, Changsha, China, August 18-21, 2010. Proceedings
From these data, we can easily find that there is a great improvement in detection performance after we use AdBoostRF. From Figure 3, we can also know AdBoostRF has the best detection performance among three algorithms. Fig. 3. The ROC curves of three methods To further verify the validity of AdaBoostRF, the weak classifiers we have obtained are used to test the AdaBoostRF performance of dealing with overfitting problem in MIT+CMU database. This test set contains 100 images, including 70 single front faces,20 multi-faces and 10 non-faces.
The inverse mode control system possesses the ability of retaining the disturbance. However, the conventional controller shows inferior performance. Thus the superiority of the control strategy in this paper can be seen obviously. 5 Conclusions The negative high-voltage power of ECRH is a nonlinear system with high requirements, thus the traditional control method may not be effective enough. To improve the performance this paper designed a new intelligent control strategy based on the concept of inverse model control using CMAC neural network.
From the above results, one can conclude that the GPS crossover operator can generate new offspring in search space more evenly than random method or orthogonal method. And its precision isn’t confined by the space dimension. Fig. 2. Pseudocode of the temporary crossover operator before GPS crossover operator used by our approach. 5 . But when come to such problems that scope of decision variables are very large and the number of the points generated by the GPS crossover operator is very small, that is, the points is distributed sparsely in the search space.