Patent ID: 7472100

Claim:
A method of training a multi-layer network used in conjunction with a physics-based engine model to define a hybrid model for modeling a gas turbine engine used for flight comprising: inputting an input signal to the gas turbine engine for operational control and to the physics-based engine model, wherein the input signal comprises a plurality of parameters; combining output parameters from the gas turbine engine with corresponding parameters estimated by the physics-based engine model to form a residual for each parameter, the gas turbine output parameters and physics-based engine model estimated parameters are responsive to the input signal parameters; deriving a mean and a standard deviation for each parameter of a first predetermined number of the input signals; deriving a mean and a standard deviation for residuals responsive to the first predetermined number of the input signals; associating a mean Mach and a mean altitude, wherein the Mach and altitude are input signal parameters, with one of a plurality of flight envelope cells, wherein if a parameter of the input signal or a responsive residual parameter is included in a pre-existing statistical distribution, the pre-existing distribution is used for training the multi-layer network, and if the mean Mach and the mean altitude are not included in a pre-existing statistical distribution, a new statistical distribution is created comprising the mean and standard deviation for each parameter of the first predetermined number of the input signals, the mean and standard deviation for residuals responsive to the first predetermined number of the input signals, and the first predetermined number value.