Neural network signal processor
First Claim
1. In a neural network having a plurality of neurons adapted to receive signals and adapted to present an output, a plurality of connective synapses providing a weighted coupling between said neurons, said neural network being capable of adapting itself to produce a predetermined output in response to an input by changing the value of said weights, the improvement comprising:
- a plurality of input neurons adapted to receive external input signals;
means for directing selected, consecutive portions of said input signal directly into said input neurons;
means for advancing said input signal so that the entire input signal from beginning to end is directed to each of said input neurons; and
means for changing said weights to produce said predetermined output during a training procedure each time a portion of a training input is directed to said input neurons, whereby after a plurality of said training procedures, said neural network will respond with said predetermined output to an input signal that is similar to said training input.
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Accused Products
Abstract
A neural network signal processor (NSP) (20) that can accept, as input, unprocessed signals (32), such as those directly from a sensor. Consecutive portions of the input waveform are directed simultaneously to input processing units, or "neurons" (22). Each portion of the input waveform (32) advances through the input neurons (22) until each neuron receives the entire waveform (32). During a training procedure, the NSP 20 receives a training waveform (30) and connective weights, or "synapses" (28) between the neurons are adjusted until a desired output is produced. The NSP (20) is trained to produce a single response while each portion of the input waveform is received by the input neurons (22). Once trained, when an unknown waveform (32) is received by the NSP (20), it will respond with the desired output when the unknown waveform (32) contains some form of the training waveform (30).
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Citations
27 Claims
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1. In a neural network having a plurality of neurons adapted to receive signals and adapted to present an output, a plurality of connective synapses providing a weighted coupling between said neurons, said neural network being capable of adapting itself to produce a predetermined output in response to an input by changing the value of said weights, the improvement comprising:
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a plurality of input neurons adapted to receive external input signals; means for directing selected, consecutive portions of said input signal directly into said input neurons; means for advancing said input signal so that the entire input signal from beginning to end is directed to each of said input neurons; and means for changing said weights to produce said predetermined output during a training procedure each time a portion of a training input is directed to said input neurons, whereby after a plurality of said training procedures, said neural network will respond with said predetermined output to an input signal that is similar to said training input. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A neural network for producing a desired output in response to a particular input signal comprising:
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a plurality of neurons adapted to receive signals and adapted to produce an output; a plurality of connective synapses providing a weighted coupling between said neurons; said neural network being capable of adapting itself during a training procedure to produce a predetermined output in response to a training input by changing the strength of said weighted connections; selected ones of said neurons, designated input neurons, adapted to receive external input signals; means for directing selected consecutive portions of said input signal directly into said input neurons; means for advancing said input signal so that the entire input signal from beginning to end is directed to each of said input neurons; and means for changing said weights to produce said predetermined output during a training procedure while a training input advances through said input neurons, whereby after said training procedure said neural network will respond with said predetermined response to an input signal that is similar to said training input. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19, 20)
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21. A multilayer perceptron for classifying an input waveform comprising:
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a plurality of input neurons adapted to receive said input waveform and to produce an output that is a sigmoid function of said input waveform; a plurality of inner neurons adapted to receive said output signals from said input neurons and adapted to produce an output signal that is a sigmoid function of said received signal; a plurality for output neurons adapted to receive said output signal from said inner neurons that is a sigmoid function of said received signal; a plurality of sampling circuits each connected to one input neuron for directing selected, consecutive portions of said input waveform into said input neurons; means for transferring the output of each sampling circuit to the input of the next successive sampling circuit so that entire input waveform from beginning to end is directed to each of said input neurons in discrete steps; timing means for synchronizing the transferring of the output signal from each sampling circuit; means for training said perceptron to produce a predetermined output each time a portion of a training input is directed to said input neurons, including a means for computing the difference between said desired output and the actual output during retaining and means for minimizing the difference between said desired output and the actual output, whereby after a plurality of said training procedures, said perceptron will respond with said predetermined output to an input waveform that is similar to said training input. - View Dependent Claims (25)
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22. A method for classifying an input signal having a characteristic waveform, said method comprising the steps of:
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receiving said input signal by a network of processing units; sampling simultaneously a plurality of consecutive portions of said input signal; directing said sampled portions of said input signal to a plurality of said processing units; advancing the sampled portions of the input signal through consecutive ones of said processing units until the entire input signal is sampled; producing a plurality of intermediate signals by said processing units each of which is a function of said sampled portions of the input signal and an associated weighing function; producing an output response that is dependent upon at least one of said intermediate signals; training said network by comparing said output produced in response to a known input signal to a predetermined output, and modifying said weighing function to reduce the difference between the output produced and said desired output; and comparing the output produced in response to an unknown input widths aid predetermined output, wherein said unknown signal can be classified when said output produced matches said predetermined output. - View Dependent Claims (23, 24)
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26. An information processor for classifying an input signal made in accordance with the method comprising:
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receiving said input signal by a network of processing units; sampling simultaneously a plurality of consecutive portions of said input signal; directing said sampled portions of said input signal to a plurality of said processing units; advancing the sampled portions of the input signal through consecutive ones of said processing units until the entire input signal is sampled; producing a plurality of intermediate signals by said processing units each of which is a function of said sampled portions of the input signal and an associated weighing function; producing an output response that is dependent upon at least one of said intermediate signals; training said network by comparing said output produced in response to a known input signal to a predetermined output and modifying said weighing function to reduce the difference between the output produced and said predetermined output; and comparing the output produced in response to an unknown input with said predetermined output, wherein said unknown signal can be classified when said output produced matches said predetermined output.
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27. A method for classifying an input signal said method comprising the steps of:
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receiving said input signal by a network of processing units; sampling simultaneously a plurality of consecutive portions of said input signal; directing said sampled portions of said input signal to a plurality of said processing units; advancing the sampled portions of the input signal through consecutive ones of said processing units until the entire input signal is sampled; producing a plurality of intermediate signals by said processing units each of which is a function of said sampled portions of the input signal and an associated weighing function; producing an output response that is dependent upon at least one of said intermediate signals; training said network by comparing said output produced in response to a known input signal to a predetermined output and modifying said weighing function to reduce the difference between the output produced and said predetermined output; comparing the output produced in response to an unknown input with said predetermined output, wherein said unknown signal can be classified when said output produced matches said desired output; and setting weights in a second network to be the same as those weights in a trained network, whereby an unlimited number of trained networks may be produced from a single trained network.
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Specification