A. Brusa, Fenil Panalal Shethia, Jacopo Mecagni, N. Cavina
{"title":"Advanced, Guided Procedure for the Calibration and Generalization of\u0000 Neural Network-Based Models of Combustion and Knock Indexes","authors":"A. Brusa, Fenil Panalal Shethia, Jacopo Mecagni, N. Cavina","doi":"10.4271/03-17-02-0009","DOIUrl":"https://doi.org/10.4271/03-17-02-0009","url":null,"abstract":"In the last few years, the artificial neural networks have been widely used in\u0000 the field of engine modeling. Some of the main reasons for this are, their\u0000 compatibility with the real-time systems, higher accuracy, and flexibility if\u0000 compared to other data-driven approaches. One of the main difficulties of using\u0000 this approach is the calibration of the network itself. It is very difficult to\u0000 find in the literature procedures that guide the user to completely define a\u0000 network. Typically, the very last steps (like the choice of the number of\u0000 neurons) must be selected by the user on the base of his sensitivity to the\u0000 problem.\u0000\u0000 \u0000This work proposes an automatic calibration procedure for the artificial neural\u0000 networks, considering all the main hyper-parameters of the network such as the\u0000 training algorithms, the activation functions, the number of the neurons, the\u0000 number of epochs, and the number of hidden layers, for modeling various\u0000 combustion indexes in a modern internal combustion engine. However, the proposed\u0000 procedure can be applied to the training of any neural network-based model.\u0000\u0000 \u0000The automatic calibration procedure outputs a configuration of the network,\u0000 giving the optimal combination in terms of hyper-parameters. The decision of the\u0000 optimal configuration of the neural network is based on a self-developed\u0000 formula, which gives a rank of all the possible hyper-parameter combinations\u0000 using some statistical parameters obtained comparing the simulated and the\u0000 experimental values. In the end, the lowest rank is selected as the optimal one\u0000 as it represents the combination having the lowest error. Following the\u0000 definition of this rank, high accuracy on the results has been achieved in terms\u0000 of the root mean square error index, for example, on the combustion phase model,\u0000 the error is 0.139°CA under steady-state conditions. On the maximum in-cylinder\u0000 pressure model, the error is 1.682 bar, while the knock model has an error of\u0000 0.457 bar for the same test that covers the whole engine operating field.","PeriodicalId":47948,"journal":{"name":"SAE International Journal of Engines","volume":"74 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86156188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}