Abstract
Owing to the complex nature of many systems, such as precision machinery design and manufacturing, advanced chemical process, the underlying physicochemical phenomenon is seldom fully understood. As the empirical methods, artificial neural networks and genetic algorithms are used to model and optimize a complex nonlinear system for increasing productivity and saving costs. In this paper, we propose an adaptive modeling and optimization method based on artificial neural network and genetic algorithm for the complex production process. The trained artificial neural network can be objective function, and then, a system model is set up. Genetic algorithm is used to optimize the input space of the neural network model to find the optimum settings for maximum products production. Using this procedure, experimental data reported in the literature were used to build a neural network model that has been effectively integrated to create a powerful tool for process modeling and optimization.
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Acknowledgments
The work described in this study is partially supported by the Natural Science Foundation Project of CQCSTC under Grant No. 2010BB2285 and the Research Foundation Project of Chongqing University of Science & Technology under Grant No. CK2010B06.
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Ge, J., Li, T. (2013). Modeling of Complex Production Process Based on Artificial Neural Networks and Genetic Algorithm. In: Zhong, Z. (eds) Proceedings of the International Conference on Information Engineering and Applications (IEA) 2012. Lecture Notes in Electrical Engineering, vol 217. Springer, London. https://doi.org/10.1007/978-1-4471-4850-0_42
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DOI: https://doi.org/10.1007/978-1-4471-4850-0_42
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