US 2003/0144974 A1 · Self organizing learning petri nets

Abstract

A self organizing learning Petri net modeling a system using a large number of training samples performs consecutive trainings using the training samples with a pre-known output value with respect to an input, and when following training samples are applied to a first system parameter created by pre-tested training samples, begins to create the system according to a method of creating a distinct system parameter when an error between an output value of the system and a pre-known output value of the following training samples is larger than a critical value, and adding the new system parameter to a pre-organized first system parameter. A final system parameter is determined by consecutively learning the large number of the training samples in an organized system again. Through this self organizing process, system modeling can be performed more accurately, and a learning process much faster than a back-propagation learning process using a unified CPN and LPN in a general neural network can be achieved.

Claims24

Dependent claims

A method in a self organizing learning Petri net, the method comprising:

training a number of training samples with a pre-known output value by comparing an error between an output value created by applying following training samples to a first system parameter produced by a pre-tested training sample and the pre-known output value of the following training samples, with a critical value, and by self organizing an initial system parameter according to a result of the comparing the error with the critical value;
and
determining a final system parameter through consecutive learning of the number of the training samples in a system established based on the initial system parameter.

A method in a self organizing learning Petri net, the method comprising:

forming a first system parameter from a first training sample;
applying a second training sample having a pre-known output value to a first system formed by the first system parameter to generate an output, the first system having a first fuzzy rules matching layer;
generating an error between the output of the system according to the second training sample and the pre-known output value of the second training sample;
comparing the error with a critical value;
and
amending the first system to a second system when the error is greater than the critical value, the second system having a second fuzzy rules matching layer different from the first fuzzy rules matching layer of the first system.

3 more elements

Dependencies are inferred from the claim text.

Self organizing learning petri nets

Life of this patent

Self organizing learning petri nets

Life of this patent

Abstract

Claims24