Calibration optimization system and optimization method for ignition timing of hydrogen internal combustion engine based on l-m neural network
An ignition timing and neural network technology, which is applied in the field of hydrogen internal combustion engine ignition timing calibration optimization system, can solve the problems of heavy experimental workload, etc., and achieve the effect of reducing manual test operation process, simple and easy algorithm, and few iterations
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Embodiment 1
[0036] Embodiment one, see figure 1 As shown, a hydrogen internal combustion engine ignition timing calibration optimization system based on L-M neural network, including data acquisition module, signal conditioning module, control unit, power supply module, clock module, storage module, execution unit, interface circuit and monitoring module, data The acquisition module collects various working condition parameters, and transmits the collected working condition parameters to the signal conditioning module. The signal conditioning module, power supply module, clock module, storage module, execution unit, and monitoring module are respectively connected to the control unit through the interface circuit. The control unit includes a data processing unit and an ignition timing optimization control module. The data processing unit uses the L-M optimization algorithm to perform neural network optimization training based on the parameters of each working condition received by the cont...
Embodiment 2
[0039] Embodiment two, see Figure 1~3 As shown, an optimization method for calibration of ignition timing of hydrogen internal combustion engines based on L-M neural network is an optimization method based on the calibration optimization system for ignition timing of hydrogen internal combustion engines based on L-M neural network described in Embodiment 1, which specifically includes Follow the steps below:
[0040] Step 1. Carry out the ignition timing calibration test of the hydrogen internal combustion engine, select the working condition parameters under different working conditions, conduct tests on different working conditions, and obtain the best ignition advance angle for the corresponding working conditions, as the calibration data, select part of the calibration data The data is used as a training sample, and the other part is used as a test sample;
[0041] Step 2. Establish an ignition timing optimization model, first establish a neural network structure model, ...
Embodiment 3
[0048] Embodiment three, see Figure 1~4 As shown, an optimization method for calibration of ignition timing of hydrogen internal combustion engines based on L-M neural network is an optimization method based on the calibration optimization system for ignition timing of hydrogen internal combustion engines based on L-M neural network described in Embodiment 1, which specifically includes Follow the steps below:
[0049] Step 1. Carry out the ignition timing calibration test of the hydrogen internal combustion engine, select the working condition parameters under different working conditions, conduct tests on different working conditions, and obtain the best ignition advance angle for the corresponding working conditions, as the calibration data, select part of the calibration data The data is used as a training sample, and the other part is used as a test sample;
[0050] Step 2. Establish an ignition timing optimization model, first establish a neural network structure model...
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