Neural network image processing system

Claim 2

a neural-simulating system for processing an input stimulus, comprising: a plurality of layers, each layer receiving layer input signals and generating layer output signals, said layer input signals including signals from the input stimulus and ones of said layer output signals from only previous layers within said plurality of layers; each of said plurality of layers including a plurality of neurons operating in parallel on said layer input signals applied to said plurality of layers; each of said neurons deriving neuron output signals from a continuously differentiable transfer function for each of said neurons based upon a combination of sets of weights associated within said neurons and said layer input signals; said plurality of neurons with a layer being arranged in groups of neurons operating in parallel on said layer input signals; and weight constraining means associated with each of said group of neurons for causing each of said groups of neurons to extract a different feature from said layer input signals and for further causing each neuron with a group of neurons to operate on a different portion of said layer input signals.

Claim 3

The neural-simulating system of claim 1 and further including: adaptive network means associated with each neuron for generating a weight correction signal based upon gradient estimate signals and convergence factors signals of each neuron and for processing said weight correction signals to thereby modify said weights associated with each neuron; and weight correction signal constraining means associated with each neuron within said groups of neurons for causing each neuron with a group of said neurons to extract the same feature from said layer input signals and for further causing each neuron with a group of neurons to operate on a different portion of said layer input signals.

Claim 4

A neural-simulating system for an image processing system, comprising: a plurality of layers, each layer receiving layer input signals and generating layer output signals, said layer input signals including signals from the image and ones of said layer output signals from only previous layers within said plurality of layers; each of said plurality of layers including a plurality of neurons operating in parallel on said layer input signals applied to said plurality of layers; each of said neurons deriving neuron output signals from a continuously differentiable transfer function for each of said neurons based upon a combination of sets of weights associated with said neurons and said layer input signals; said plurality of neurons within a layer being arranged in groups of neurons operating in parallel on said layer input signals; and weight constraining means associated with each of said groups of neurons for causing each of said groups of neurons to extract a different feature from said layer input signals and for further causing each neuron with a group of neurons to operate on a different portion of said layer input signals.

Claim 5

A neural-simulating system for an image processing system, comprising: a plurality of layers, each layer receiving layer input signals and generating layer output signals, said layer input signals including signals from the image and ones of said layer output signals from only previous layers within said plurality of layers; each of said plurality of layers including a plurality of neurons operating in parallel on said layer input signals applied to said plurality of layers; each of said neurons deriving neuron output signals from a continuously differentiable transfer function for each of said neurons based upon a combination of sets of weights associated with said neurons and said layer input signals; said plurality of neurons within a layer being arranged in groups of neurons operating in parallel on said layer input signals; and weight constraining means associated with each of said groups of neurons for causing each of said groups of neurons to extract a different feature from said layer input signals and for further causing each neuron with a group of neurons to operate on a different portion of said layer input signals.

Claim 6

The neutral-simulating system of claim 3 and further including: adaptive network means associated with each neuron for generating a weight correction signal based upon gradient estimate signals and convergence factors signals of each neuron and for processing said weight correction signals to thereby modify said weights associated with each neuron; and weight correction signal constraining means associated with each neuron within one of said groups of neurons for causing each neuron with a group of said neurons to extract the same feature from said layer input signals and for further causing each neuron with a group of neurons to operate on a different portion of said layer input signals.

Claim 7

A neutral-simulating system which is trained on a set of input patterns to the system for subsequent identification of unknown patterns, comprising: clustering means for extracting a training set of patterns representing samples of the input patterns and for updating the training set from the input patterns to the system; a plurality of layers, each layer receiving layer input signals and generating layer output signals, said layer input signals including signals from the training set and ones of said layer output signals from only previous layers within said plurality of layers; each of said plurality of layers including a plurality of neurons operating in parallel on said layer input signals applied to said plurality of layers; each of said neurons deriving neuron output signals from a continuously differentiable transfer function for each of said neurons based upon a combination of sets of weights associated with said neurons and said layer input signals; and adaptive network means associated with each neuron for generating a weight correction signal based upon gradient estimate signals and convergence factors signals of each neuron and for processing said weight correction signals to thereby modify said weights associated with each neuron.

Claim 8

A neutral-simulating system which is trained on a set of input patterns to the system for subsequent identification of unknown patterns, comprising: clustering means for extracting a training set of patterns representing samples of the input patterns and for updating the training set from the input patterns to the system; a plurality of layers, each layer receiving layer input signals and generating layer output signals, said layer input signals including signals from the training set and ones of said layer output signals from only previous layers within said plurality of layers; each of said plurality of layers including a plurality of neurons operating in parallel on said layer input signals applied to said plurality of layers; each of said neurons deriving neuron output signals from a continuously differentiable transfer function for each of said neurons based upon a combination of sets of weights associated with said neurons and said layer input signals; and adaptive network means associated with each neuron for generating a weight correction signal based upon gradient estimate signals and convergence factors signals of each neuron and for processing said weight correction signals to thereby modify said weights associated with each neuron.

Claim 9

The neural-simulating system of claim 5 and further including: said plurality of neurons within a layer being arranged in groups of neurons operating in parallel on said layer input signals; weight constraining means associated with each of said groups of neurons for causing each of said groups of neurons to extract a different feature from said layer input signals and for further causing each neuron with a group of neurons to operate on a different portion of said layer input signals; and weight correction signal constraining means associated with each neuron within one of said groups of neurons for causing each neuron with a group of said neurons to extract the same feature from said layer input signals and for further causing each neuron with a group of neurons to operate on a different portion of said layer input signals.