Diesel Engine Combustion Control with Triple Fuel Injections based on Cerebellar Model Articulation Controller (CMAC) in Feedback Error Learning

Tamura, Taro; Eguchi, Makoto; Qiao, Maoying; Ohmori, Hitoshi

doi:10.1299/jmsesdm.2017.9.c110

Cited by 2 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the studies on the error bound of CMAC are insufficient (Lin & Chiang, 1997;Lin & Wang, 1996). Physical storage size on the computer required by data is usually the first parameter to be determined (Lin & Wang, 1996;Tamura et al, 2017). This method is feasible for simple models and small data volumes.…”

Section: Introductionmentioning

confidence: 99%

Prediction and Optimization of a Desulphurization System Using Cmac Neural Network and Genetic Algorithm

Kong

Zhang

Wang

et al. 2020

Journal of Environmental Engineering and Landscape Management

View full text Add to dashboard Cite

In this paper, taking desulphurizing ratio and economic cost as two objectives, a ten-input two-output prediction model was structured and validated for desulphurization system. Cerebellar model articulation controller (CMAC) neural network and genetic algorithm (GA) were used for model building and optimization of cost respectively. In the model building process, the grey relation entropy analysis and uniform design method were used to screen the input variables and study the model parameters separately. Traditional regression analysis and proposed location number analysis method were adopted to analyze output errors of experiment group and predict the results of test group. Results show that regression analyses keep high fit degree with experiment group results while the fitting accuracies for test group are quite different. As for location number analysis, a power function between output errors and location numbers was fitted well with the data of experiment group and test group for SO2. Prediction model was initialized by location number analysis method. Model was validated and cost optimization case was performed with GA subsequently. The result shows that the optimal cost obtained from GA could be reduced by more than 30% compared with original optimal operating parameters under same constraints.

show abstract