초록 ▼

In the design and implementation of neural networks, training is determined by a series of architectural and parametric decisions. A method is disclosed that, using genetic algorithms, improves the training characteristics of a neural network. The method begins with a population and iteratively modi

In the design and implementation of neural networks, training is determined by a series of architectural and parametric decisions. A method is disclosed that, using genetic algorithms, improves the training characteristics of a neural network. The method begins with a population and iteratively modifies one or more parameters in each generation based on the network with the best training response in the previous generation.

대표청구항 ▼

[ What I claim is:] [1.] A method of producing an artificial neural network wherein an output response to training is dependent upon at least a parameter value and for use in a computer, the method comprising the steps of:a) forming a plurality of groups of individual artificial neural networks, eac

[ What I claim is:] [1.] A method of producing an artificial neural network wherein an output response to training is dependent upon at least a parameter value and for use in a computer, the method comprising the steps of:a) forming a plurality of groups of individual artificial neural networks, each individual in each group having a unique parameter value, the parameter values defining a first broad range of values, the individuals within a group being related to each other by the closeness of their parameter value;b) using the computer, applying to each individual a plurality of input stimuli and their corresponding expected output responses;c) using the computer, testing the individuals by applying to each individual at least an input stimulus and comparing at least a corresponding output response to at least a corresponding expected output response to determine the parameter value that is a best fit, wherein the best fit is assigned to the parameter value of the individual that provides a closest output response to the expected output response;d) using the computer, assigning new parameter values to the plurality of groups of individuals based on the parameter value that is the best fit, the new parameter values defining a second range of values; and,e) repeating steps (b) to (d) until an individual in a group is within a predetermined tolerance of the at least an expected output response.