Abstract
In this paper, an optimization system is established based on a hybrid neural network and genetic algorithm approach. The application program is compiled in Matlab engineering computing language, which is used in calculating the parameter value predicted by neural network and the result of genetic algorithm optimization .The comparison and error analysis has been carried out between the results predicted by network and CAE simulated results, which shows that the BP network is stable and reliable. The optimized outcome verified by CAE simulation and tested by experiment has been proved to be correct. It has been bean indicated that the injection parameter optimization method based on the hybrid neural network and genetic algorithm approach is feasible.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Wang, B., Gao, F., Yue, P.: Neural Network Approach to Predict Melt Temperature in Injection Processes. Chinese J. of Chem. Eng. 8, 326–331 (2000)
Liu, J., Zhong, W.C., Liu, F., et al.: A Novel Clustering Based on the Immune Evolutionary Algorithm. ACTA Electronic SWICA, 1072–1860 (2001)
Tiana, P., David, K.: Hybrid Neural Models for Pressure Control in Injection Molding. Advances in Polymer Technology 18, 19–31 (1999)
Liu, Z.Y., Lu, J.H., Chen, L.J.: A Novel RBF Neural Network and Its Application in Thermal Processes Modeling. In: Proceedings of the CSEE, pp. 8–122 (2002)
Mok, S., Kwong, C.K., Lau, W.S.: A Hybrid Neural Network and Genetic Algorithm Approach to the Determination of Initial Process Parameters for Injection Molding. The Imitational Journal of Advanced Manufacturing Technology 18, 404–409 (2001)
Maulik, U., Bandyopdhyay, S.: Performance Evaluation of Some Clustering Algorithms and Validity Indices. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1650–1654 (2002)
Hou, Y.W., Shen, J., Li, Y.G.: A Simulation Study on Load Modeling of A Thermal Power Unit Based on Wavelet Neural Networks. In: Proceedings of the CSEE, pp. 220–224 (2003)
Jiang, W.J.: Research on the Learning Algorithm of BP Neural Networks Embedded in Evolution Strategies. In: WCICA 2005, 222–227 (2005)
Xu, Q., Wen, X.R.: High Precision Direct Integration Scheme for Structural Dynamic Load Identification. Chinese J. of Computational Mechanics, 53–57 (2002)
Wu, J.Y., Wang, X.C.: A Parallel Genetic Design Method With Coarse Grain. Chinese J. of Computational Mechanics, 148–153 (2002)
Li, S.J., Liu, Y.X.: Identification of Structural Vibration Parameter Based on Genetic Algorithm. J. of Chinese University of Mining Science and Technology, 256–260 (2001)
Yarlagadda, P.K.D.V.: Prediction of Processing Parameters for Injection Molding by Using a Hybrid Neural Network. Proc. Instn. Mech. Engrs. 215, 1465–1470 (2001)
Weijin, J.: Research on the Optimization of the Equipment Fund’s Assignment Model Based on HGA. Journal of the Control and Instruments in Chemical Industry 31(2), 10–14 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, Y., Wang, R., Yi, G. (2009). Research a Novel Optimization Mechanism of Parameters Based on Hybrid NN and GA. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01510-6_52
Download citation
DOI: https://doi.org/10.1007/978-3-642-01510-6_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01509-0
Online ISBN: 978-3-642-01510-6
eBook Packages: Computer ScienceComputer Science (R0)