Affiliation:
1. Hangzhou Dianzi University
Abstract
The recursive compensatory fuzzy neural network model was established against the characteristics of temperature control for film laminating machine. The neural network can be used to construct the fuzzy system, and the self-adaptive and self-learning capability of neural networks was used to automatically adjust fuzzy system parameters, BP network could be learned and trained by the gradient descent algorithm. Based on the test data for the study and testing of network, system error is less than the national standard error requirements, the results proved the effectiveness and feasibility of the algorithm.
Publisher
Trans Tech Publications, Ltd.
Reference5 articles.
1. Guan L, Lin J. Study on the offset color reproduction control system based on fuzzy neural network. Advances in Neural Networks–ISNN 2009, Springer Berlin Heidelberg(2009).
2. Miikkulainen, R. Evolving Neural Networks. In: Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference, p.3415–3434. IEEE Press, London (2007).
3. Luo, Y.C., Guo, L.H., Kang, C.Q. Assessing Threat Degree of Aerial Target by Applying Rough Sets and Fuzzy Neural Networks. Opto-Electronic Engineering, 10–15 (2008).
4. Zhang, N.R., Yan, P.F. Neural Networks and Fuzzy Control. Tsinghua University Press, Beijing (2005).
5. Hu, Y.L., Cao, J.G., Qiao, J.F. Fuzzy Neural Network Control of Activated Sludge System. Journal of System Simulation, 2541–2544 (2005).