Asymmetric wavelet kernel in support vector learning
AssigneeToyota Motor Engineering & Manufacturing North America, Inc.
InventorsZhao Lu, Jing Sun, Kenneth R. Butts
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Abstract
Example methods of modeling a nonlinear dynamical system such as a vehicle engine include providing a model using linear programming support vector regression (LP-SVR) having an asymmetric wavelet kernel, such as derived from a raised-cosine wavelet function. The model may be trained to determine parallel model parameters while in a series-parallel configuration, and operated in the parallel configuration allowing improved and more flexible model performance. An improved engine control unit may use an LP-SVR with an asymmetric wavelet kernel.
Claims14
A method of modeling a system, the system being a nonlinear dynamical system, the method comprising:
- providing a model, the model being a linear programming support vector regression (LP-SVR) having an asymmetric wavelet kernel, the asymmetric wavelet kernel being derived from a raised-cosine wavelet function;
- training the model to determine model parameters, the model being trained in a series-parallel configuration;
- and
- modeling the system, using the model in a parallel configuration and the model parameters determined in the series-parallel configuration.
A method of modeling a system, the system being a vehicle engine, the method comprising:
- providing a model, the model including linear programming support vector regression (LP-SVR) having a wavelet kernel,
- the model being implemented as software on an engine control unit,
- the wavelet kernel being an asymmetric wavelet kernel derived from a raised-cosine wavelet function;
- training the model to determine model parameters, the model being trained in a series-parallel configuration;
- and
- modeling the system, using the model in a parallel configuration, using the model parameters determined in the series-parallel configuration.
An apparatus, the apparatus being an electronic control unit for a vehicle engine, the apparatus comprising:
- engine data inputs configured to receive engine data, the engine data including engine operation data and operator input data;
- engine control outputs, operable to control engine parameters, the engine parameters including fuel injection quantity;
- and
- an electronic control circuit, the electronic control circuit comprising at least a memory and a processor, the electronic circuit using a linear programming support vector regression (LP-SVR) having a wavelet kernel as a model of the vehicle engine, the model being used to determine required engine model from the engine data,
- the wavelet kernel being an asymmetric wavelet kernel derived from a raised-cosine wavelet function.
Dependencies are inferred from the claim text.