By Chunhua Feng, Zhenkun Huang (auth.), De-Shuang Huang, Phalguni Gupta, Xiang Zhang, Prashan Premaratne (eds.)
This ebook constitutes the refereed court cases of the eighth overseas convention on clever Computing, ICIC 2012, held in Huangshan, China, in July 2012. The 242 revised complete papers provided within the 3 volumes LNCS 7389, LNAI 7390, and CCIS 304 have been conscientiously reviewed and chosen from 753 submissions. The papers during this quantity (CCIS 304) are equipped in topical sections on Neural Networks; Particle Swarm Optimization and area of interest expertise; Kernel tools and aiding Vector Machines; Biology encouraged Computing and Optimization; wisdom Discovery and knowledge Mining; clever Computing in Bioinformatics; clever Computing in development acceptance; clever Computing in photo Processing; clever Computing in desktop imaginative and prescient; clever regulate and Automation; wisdom Representation/Reasoning and specialist structures; Advances in info protection; Protein and Gene Bioinformatics; gentle Computing and Bio-Inspired Techiques in Real-World functions; Bio-Inspired Computing and Applications.
Read Online or Download Emerging Intelligent Computing Technology and Applications: 8th International Conference, ICIC 2012, Huangshan, China, July 25-29, 2012. Proceedings PDF
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Extra resources for Emerging Intelligent Computing Technology and Applications: 8th International Conference, ICIC 2012, Huangshan, China, July 25-29, 2012. Proceedings
Sample text
Acknowledgments. This work was jointly supported by National Natural Science Foundation of China, grant No. 60775044 and 61105063, and Program for New Century Excellent Talents in Universities, grant No. NCET-07-0802. References 1. : Evolutionary Algorithms for Multi-objective Optimization Problems with Interval Parameters. In: 5th IEEE International Conference on Bio-Inspired Computing: Theories and Applications, pp. 411–420 (2010) 2. : Clone Selection Algorithm to Solve Prefer-ence Multi-objective Optimization.
Using CATS benchmark, the proposed model was confirmed its priority to conventional neural network model MLP, and mathematic model ARIMA. Though the prediction precision did not achieve the highest level of the best of IJCNN’04 prediction competition participant method, this work firstly shows that RBM is available to be used as a predictor for time series forecasting. References 1. : Results of the NN3 neural network forecasting competition. In: The 27 th International Symposium on Forecasting, Program, vol.
1, where M scd , M sc are the setpoints and tracking results of sucrose crystal masses; M gcd , M gc are the setpoints and tracking results of glucose crystal masses. 2, where, Csd , Cs are the setpoints and tracking results of sucrose concentration; Cgd , Cg are the setpoints and tracking results of glucose concentration. 0 40 50 60 70 80 t/min Fig. 1. 5 0 10 20 30 40 50 60 70 80 90 100 t/min Fig. 2. Concentration tracking curves From the simulation results in figure 1, we get to know that the crystal masses of sucrose-glucose track their respective setpoints commendably.