NEURAL NETWORK AND METHOD OF NEURAL NETWORK TRAINING
First Claim
1. A neural network comprising:
- a plurality of inputs of the neural network, each input configured to receive an input signal having an input value;
a plurality of synapses, wherein each synapse is connected to one of the plurality of inputs and includes a plurality of corrective weights, wherein each corrective weight is defined by a weight value;
a set of distributors, wherein each distributor is operatively connected to one of the plurality of inputs for receiving the respective input signal and is configured to select one or more corrective weights from the plurality of corrective weights in correlation with the input value;
a set of neurons, wherein each neuron has at least one output and is connected with at least one of the plurality of inputs via one of the plurality of synapses, and wherein each neuron is configured to add up the weight values of the corrective weights selected from each synapse connected to the respective neuron and thereby generate a neuron sum; and
a weight correction calculator configured to receive a desired output signal having a value, determine a deviation of the neuron sum from the desired output signal value, and modify respective corrective weight values using the determined deviation, such that adding up the modified corrective weight values to determine the neuron sum minimizes the deviation of the neuron sum from the desired output signal value to thereby train the neural network.
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Abstract
A neural network includes a plurality of inputs for receiving input signals, and synapses connected to the inputs and having corrective weights. The network additionally includes distributors. Each distributor is connected to one of the inputs for receiving the respective input signal and selects one or more corrective weights in correlation with the input value. The network also includes neurons. Each neuron has an output connected with at least one of the inputs via one synapse and generates a neuron sum by summing corrective weights selected from each synapse connected to the respective neuron. Furthermore, the network includes a weight correction calculator that receives a desired output signal, determines a deviation of the neuron sum from the desired output signal value, and modifies respective corrective weights using the determined deviation. Adding up the modified corrective weights to determine the neuron sum minimizes the subject deviation for training the neural network.
9 Citations
16 Claims
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1. A neural network comprising:
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a plurality of inputs of the neural network, each input configured to receive an input signal having an input value; a plurality of synapses, wherein each synapse is connected to one of the plurality of inputs and includes a plurality of corrective weights, wherein each corrective weight is defined by a weight value; a set of distributors, wherein each distributor is operatively connected to one of the plurality of inputs for receiving the respective input signal and is configured to select one or more corrective weights from the plurality of corrective weights in correlation with the input value; a set of neurons, wherein each neuron has at least one output and is connected with at least one of the plurality of inputs via one of the plurality of synapses, and wherein each neuron is configured to add up the weight values of the corrective weights selected from each synapse connected to the respective neuron and thereby generate a neuron sum; and a weight correction calculator configured to receive a desired output signal having a value, determine a deviation of the neuron sum from the desired output signal value, and modify respective corrective weight values using the determined deviation, such that adding up the modified corrective weight values to determine the neuron sum minimizes the deviation of the neuron sum from the desired output signal value to thereby train the neural network. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A method of training a neural network, comprising:
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receiving, via an input to the neural network, an input signal having an input value; communicating the input signal to a distributor operatively connected to the input; selecting, via the distributor, in correlation with the input value, one or more corrective weights from a plurality of corrective weights, wherein each corrective weight is defined by a weight value and is positioned on a synapse connected to the input; adding up the weight values of the selected corrective weights, via a neuron connected with the input via the synapse and having at least one output, to generate a neuron sum; receiving, via a weight correction calculator, a desired output signal having a value; determining, via the weight correction calculator, a deviation of the neuron sum from the desired output signal value; and modifying, via the weight correction calculator, respective corrective weight values using the determined deviation, such that adding up the modified corrective weight values to determine the neuron sum minimizes the deviation of the neuron sum from the desired output signal value to thereby train the neural network. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16)
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Specification