Patent ID: 7457788

Claim:
A method of reducing number of computations when modeling several systems using a neural network, wherein said neural network contains a plurality of neurons, wherein each system is modeled by starting with a corresponding plurality of initial weights for said plurality of neurons and performing computations iteratively to re-compute weights of at least one of said neurons until a pre-specified condition is obtained, wherein the weights of said neurons when said pre-specified condition is obtained represents a set of final weights modeling the system, said method comprising: receiving a first data set characterizing the behavior of a first system which has not been previously modeled, said first data set containing a first plurality of data elements; modeling said first system based on said first data set using said neural network, wherein a first set of weights are generated by said modeling said first system, wherein said first set of weights corresponds to the set of final weights associated with said plurality of neurons modeling said first system; receiving a second data set characterizing the behavior of a second system which is distinct from said first system, has not been previously modeled and is sought to be modeled by said neural network, said second data set containing a second plurality of data elements; determining whether said first plurality of data elements follow a similar pattern as said second plurality of data elements; and modeling said second system based on said second data set using said neural network to generate a second set of weights as the set of final weights for said second system, wherein the final set of weights of said previously modeled first system are used as initial weights for said plurality of neurons while modeling said second system if said first plurality of data elements follow a similar pattern as said second plurality of data elements, wherein using the final set of weights of said previously modeling first initial weights to model said second system reduces the number of computations in modeling said second system.