Abstract: In various embodiments, methods, systems, and vehicle apparatuses are provided. A method for implementing torque control using a Neural Network (NN) for a torque prediction model to receive a set of measured vehicle operating inputs associated with torque prediction; substituting a set of multiple independent variables into the torque prediction model so that the NN is then taking the form of a simplified pseudo-NN that contains a reduced variable set of one independent variable; processing, the set of measured vehicle operating inputs by the pseudo-NN based on the NN prediction model by using only one independent variable in a pseudo-NN's simplified mathematical expression; and solving for at least one root of the pseudo-NN's simplified mathematical expression by obtaining a root value without having to rely on an inversion operation of a mathematical expression that consists of an entire set of independent variables.

Abstract: In various embodiments, methods, systems, and vehicle apparatuses are provided. A method for implementing torque control using a Neural Network (NN) for a torque prediction model to receive a set of measured vehicle operating inputs associated with torque prediction; substituting a set of multiple independent variables into the torque prediction model so that the NN is then taking the form of a simplified pseudo-NN that contains a reduced variable set of one independent variable; processing, the set of measured vehicle operating inputs by the pseudo-NN based on the NN prediction model by using only one independent variable in a pseudo-NN's simplified mathematical expression; and solving for at least one root of the pseudo-NN's simplified mathematical expression by obtaining a root value without having to rely on an inversion operation of a mathematical expression that consists of an entire set of independent variables.

Abstract: Methods and systems are provided for monitoring a fuel injector of an internal combustion engine. In one embodiment, a method includes: receiving a set of feature data, the feature data sensed from a fuel injector during a fuel injection event; processing, by a processor, the set of feature data with a decision tree model to generate a prediction of a fault status; and selectively generating, by the processor, a notification signal based on the prediction.

Abstract: Methods and systems are provided for monitoring a fuel injector of an internal combustion engine. In one embodiment, a method includes: receiving a set of feature data, the feature data sensed from a fuel injector during a fuel injection event; processing, by a processor, the set of feature data with a decision tree model to generate a prediction of a fault status; and selectively generating, by the processor, a notification signal based on the prediction.

Abstract: Methods and systems are provided for monitoring a fuel injector of an internal combustion engine. In one embodiment, a method includes: receiving a set of feature data, the feature data sensed from a fuel injector during a fuel injection event; processing, by a processor, the set of feature data with a machine learning model to generate a prediction of a fault status; and selectively generating, by the processor, a notification signal based on the prediction.