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Toru Eguchi,Takaaki Sekiai,Akihiro Yamada,Satoru Shimizu,Masayuki Fukai 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
We previously proposed a control technology to reduce CO and NOx emissions in power generating. In this technology, an optimal controllogic is obtained by Rein forcement Learning (RL) and a Radial Basis Function(RBF) network which constructs are sponse surface for the CO and NOx properties. An improvement of estimation accuracy of the response surface can enhance the controllogic performance, so the radius of RBF network should be determined properly since it is one of the most influential factors on estimation accuracy. On the other hand, adjustment of the radius should be executed with in several minutes as computational time for constructing the response surface is restricted. In this paper, we propose a new radiusad justing method for RBF network stoachieve high estimation accuracy and short computational time. This method adjustsradii based on the densities of learning data, thus it can achieve both high estimation accuracy and short computational time. The results of our evaluation showed that the proposed method had higher estimation accuracy than conventional methods with in apractical computatinal time.