A method and apparatus of training a neural network. The method and apparatus include creating a model for a desired function as a multi-dimensional function, determining if the created model fits a simple finite geometry model, and generating a Radon transform to fit the simple finite geometry model. The desired function is fed through the Radon transform to generate weights. A multilayer perceptron of the neural network is trained using the weights.
This application claims the benefit of U.S. Provisional Application No. 60178060, "METHOD OF USING NEURAL NET TO TRAIN A NEURAL NET", filed Jan. 24, 2000.
Methods and systems for analyzing engine unbalance conditions are disclosed. In one embodiment, a method includes receiving vibrational data from a plurality of locations distributed over an engine and a surrounding engine support structure, and inputting the vibrational data into a neural network inverse model. The neural network inverse model establishes a relationship between the vibrational data and an unbalance condition of the engine, and outputs diagnostic information indicating the unbalance condition of the engine. In a further embodiment, a method further includes subjecting the vibrational data to a Fast Fourier Transformation to extract a desired once per revolution vibrational data prior to input to the neural network inverse model.
Methods and systems for analyzing engine unbalance conditions are disclosed. In one embodiment, a method includes receiving vibrational data from a plurality of locations distributed over an engine and a surrounding engine support structure, and inputting the vibrational data into a neural network inverse model. The neural network inverse model establishes a relationship between the vibrational data and an unbalance condition of the engine, and outputs diagnostic information indicating the unbalance condition of the engine. In a further embodiment, a method further includes subjecting the vibrational data to a Fast Fourier Transformation to extract a desired once per revolution vibrational data prior to input to the neural network inverse model.
Methods and systems for analyzing engine unbalance conditions are disclosed. In one embodiment, a method includes receiving vibrational data from a plurality of locations distributed over an engine and a surrounding engine support structure, and inputting the vibrational data into a neural network inverse model. The neural network inverse model establishes a relationship between the vibrational data and an unbalance condition of the engine, and outputs diagnostic information indicating the unbalance condition of the engine. In a further embodiment, a method further includes subjecting the vibrational data to a Fast Fourier Transformation to extract a desired once per revolution vibrational data prior to input to the neural network inverse model.
