In paper industry, pulp bleaching is a most important concern in order to effectively respond to the high quality standards demanded by market requirements. Thus, a good knowledge of the bleaching plant is vital to achieve those goals. In this paper a neuro-fuzzy strategy is proposed to aid bleaching quality by predicting the outlet brightness. It consists of two phases: in the first one, a fuzzy clustering technique is applied to extract a set of fuzzy rules; in the second one, the centres and widths of the membership functions are tuned by means of a fuzzy neural network trained with backpropagation. This technique seems promising since it permits good results with large nonlinear plants. Furthermore, it describes the plant using a set of linguistic rules, which have the advantage of being closer to natural human language, so, more intuitive for operators. Preliminary promising results are presented and discussed.
Choy M. C., Cheu R. L., Srinivasan D. and Logi F. (2003). “Real-time Coordinated Signal Control using Agents with Online Reinforcement Learning”, Proceedings of the 82nd Annual Meeting of the Transportation Research Board – TRB’2003.
Choy M. C., Cheu R. L., Srinivasan D. and Logi F. (2003). “Real-Time Coordinated Signal Control Through Use of Agents with Online Reinforcement Learning”, Journal of the Transportation Research Board, Vol. 1836, pp. 64-75.
Year 2002 : 1 citations
1. MC Choy, D Srinivasan, RL Cheu - the Proceedings of the …, 2002 - ascelibrary.org, “Cooperative Learning Hybrid Agents for Traffic Management and Control”
