Method and apparatus of using neural network to train a neural network

Abstract

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.

1 - 8 . (canceled)  
   
   
       9 . A method of training a first neural network using a second neural network, the method for execution by a processor and comprising: 
 receiving an input at a hidden layer of the first neural network, the first neural network representing a function;    receiving afferent input from the second neural network, the second neural network representing an inverse of the function; and    adjusting weights at the hidden layer based on the afferent input.    
   
   
       10 . The method of  claim 9 , further comprising summing results of the function at the hidden layer to produce an output, the summing based on the weights.  
   
   
       11 . The method of  claim 9 , wherein the function is a model for a simple finite geometry.  
   
   
       12 . The method of  claim 9 , further comprising: 
 constructing the first and second neural networks; and    adjusting the weights at the hidden layer until output from the second neural network is equal to the input into the first neural network.    
   
   
       13 . The method of  claim 9 , wherein the first neural network represents a Radon transform, and the second neural network represents an inverse of the Radon transform.  
   
   
       14 . A machine-readable medium having executable instructions for causing a machine to perform a method of training a first neural network using a second neural network, the method comprising: 
 receiving an input at a hidden layer of the first neural network, the first neural network representing a function;    receiving afferent input from the second neural network, the second neural network representing an inverse of the function; and    adjusting weights at the hidden layer based on the afferent input.    
   
   
       15 . The machine-readable medium of  claim 14 , wherein the method further comprises summing results of the function at the hidden layer to produce an output, the summing based on the weights.  
   
   
       16 . The machine-readable medium of  claim 14 , wherein the function is a model for a simple finite geometry.  
   
   
       17 . The machine-readable medium of  claim 14 , wherein the method further comprises: 
 constructing the first and second neural networks; and    adjusting the weights at the hidden layer until output from the second neural network is equal to the input into the first neural network.    
   
   
       18 . The machine-readable medium of  claim 14 , wherein the first neural network represents a Radon transform, and the second neural network represents an inverse of the Radon transform.  
   
   
       19 . A system comprising: 
 a processor coupled to a memory through a bus; and    a training process executed from the memory by the processor to cause the processor to receive an input at a hidden layer of a first neural network, the first neural network representing a function, receive afferent input from a second neural network, the second neural network representing an inverse of the function, and adjust weights at the hidden layer based on the afferent input.    
   
   
       20 . The system of  claim 19 , wherein the process further causes the processor to sum results of the function at the hidden layer to produce an output, the summing based on the weights.  
   
   
       21 . The system of  claim 19 , wherein the function is a model for a simple finite geometry.  
   
   
       22 . The system of  claim 19 , wherein the process further causes the processor to construct the first and second neural networks, and adjust the weights at the hidden layer until output from the second neural network is equal to the input into the first neural network.  
   
   
       23 . The system of  claim 19 , wherein the first neural network represents a Radon transform, and the second neural network represents an inverse of the Radon transform.  
   
   
       24 . The system of  claim 19 , further comprising a network interface for coupling the system to a second processor, wherein the afferent input is received by the processor through the network interface from the second processor.  
   
   
       25 . An apparatus for training a first neural network using a second neural network, the apparatus comprising: 
 means for receiving an input at a hidden layer of the first neural network, the first neural network representing a function;    means for receiving afferent input from the second neural network, the second neural network representing an inverse of the function; and    means for adjusting weights at the hidden layer based on the afferent input.