Sparse Bayesian extreme learning machine and its application to biofuel engine performance prediction

Biofuels are important for the reduction of engine exhaust emissions and fossil fuel consumption. To use different blends of biofuels, the electronic control unit (ECU) of the engine must be modified and calibrated. However, the calibration process of ECU is very costly and time-consuming. Therefore, most of the engines can only use one specific biofuel blend; otherwise the engines cannot run properly. To alleviate this problem, a mathematical engine model can be used for predicting the engine performance at different ECU settings and biofuel blends so that the ECU can be re-calibrated in real-time via some controllers. The prediction of the engine model must be very fast and accurate for such online control purpose. It must also be very compact due to the limited memory size of the ECU. As a result, a new method called sparse Bayesian extreme learning machine (SBELM) is proposed in this paper to fulfill these requirements of the mathematical engine model for fast engine performance prediction and ECU online re-calibration. Experiments were conducted to compare SBELM with conventional ELM, Bayesian ELM (BELM) and back-propagated neural network (BPNN). Evaluation results show that SBELM can perform at least similar to, but mostly better than, ELM, BELM and BPNN, in terms of prediction accuracy. In terms of execution time, model size, and insensitivity to hidden neuron number, SBELM completely outperforms the other three methods. By these results, SBELM is verified to better fulfill the practical requirements of mathematical engine model for online engine performance prediction.

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