By W. Eric Wong, Tingshao Zhu
This booklet goals to ascertain innovation within the fields of desktop engineering and networking. The booklet covers vital rising subject matters in computing device engineering and networking, and it'll support researchers and engineers enhance their wisdom of state-of-art in similar parts. The publication offers papers from The court cases of the 2013 foreign convention on machine Engineering and community (CENet2013) which used to be hung on 20-21 July, in Shanghai, China.
Read or Download Computer Engineering and Networking: Proceedings of the 2013 International Conference on Computer Engineering and Network (CENet2013) PDF
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Additional info for Computer Engineering and Networking: Proceedings of the 2013 International Conference on Computer Engineering and Network (CENet2013)
10. , & Deng, B. (2012). G1 continuous approximate curves on NURBS surfaces. Computer-Aided Design, 44(9), 824–834. Chapter 3 Computation Method of Processing Time Based on BP Neural Network and Genetic Algorithm Danchen Zhou and Chao Guo Abstract Looking-up standard processing time table is a commonly used and important determination method of processing time. However, the large error in nonstandard nodes brings adverse effect on its accuracy. In view of the problem, a computation method of processing time based on back propagation neural network (BPNN) and genetic algorithm (GA) is proposed.
Fig. 1 Geometry of 2M elements array 41 Y Observer d Am ... A3 A2 θ A1 1 2 A1 A2 3 M X A3 ... 1) can be translated as given in Eq. 3) Optimizing by GA Genetic algorithm is a stochastic global search algorithm based on the natural selection and genetic. GA is different with the traditional optimum algorithms, which are used to generate a deterministic test solution sequence merely based on the gradient calculation of evaluation function [12, 13]. Genetic algorithm searched the optimal solution through imitating the natural evolutionary process.
Design of nano-micro-composite ceramic tool and die material with back propagation neural network and genetic algorithm. Journal of Materials Engineering and Performance, 21(4), 463–470. 6. Lin, J. (2012). A systematic estimation model for fraction nonconforming of a wafer in semiconductor manufacturing research. Applied Soft Computing Journal, 12(6), 1733–1740. 7. , & Mo, J. (2011). Springback prediction of high-strength sheet metal under air bending forming and tool design based on GA-BPNN. The International Journal of Advanced Manufacturing Technology, 53(5–8), 473–483.
Computer Engineering and Networking: Proceedings of the 2013 International Conference on Computer Engineering and Network (CENet2013) by W. Eric Wong, Tingshao Zhu