Resources Contact Us Home
Browse by: INVENTOR PATENT HOLDER PATENT NUMBER DATE
 
 
Recurrent neural networks teaching system










Image Number 7 for United States Patent #5182794.

A teaching method for a recurrent neural network having hidden, output and input neurons calculates weighting errors over a limited number of propagations of the network. This process permits the use of conventional teaching sets, such as are used with feedforward networks, to be used with recurrent networks. The teaching outputs are substituted for the computed activations of the output neurons in the forward propagation and error correction stages. Back propagated error from the last propagation is assumed to be zero for the hidden neurons. A method of reducing drift of the network with respect to a modeled process is also described and a forced cycling method to eliminate the time lag between network input and output.








 
 
  Recently Added Patents
Data processing method
Lubricating oil compositions
Provision of downlink packet access services to user equipment in spread spectrum communication network
Data distribution unit for vehicle entertainment system
Method of reducing acetaldehyde in polyesters, and polyesters therefrom
Portable computer
Laser processing method and apparatus
  Randomly Featured Patents
Method for testing semiconductor components
Prefabricated room structure for facilities in general such as toilets, baths, kitchens and the like
Magnetic sensor using tunnel resistance to detect an external magnetic field
Bingo gaming system with player selected daub modes
Precipitation polymerization process for producing an oil adsorbent polymer capable of entrapping solid particles and liquids and the product thereof
Use of guanidine compounds as physiological strengthening agents in the form of nutritional supplements, animal feed additives, in cosmetic preparations and as plant stimulants
Tonometer
Method for assembling a stator for an electric machine
Recessed fluorescent luminaire housing
Fault protection scheme