Patent ID: 11953457
Assignee: YISSUM RESEARCH DEVELOPMENT COMPANY OF THE HEBREW UNIVERSITY OF JERUSALEM LTD.
Field: Measurement (Instruments)
Classification: CPC G | IPC G

Claim 14:
15. The method of claim 1, wherein the deep-learning (DL) method further comprises:
a) feeding the input string into a neural network, wherein a value of node in an input layer of said neural network is set to the value of bit x, in said input string, and outputting the values of the nodes of the input layer to subsequent hidden layers of said neural network, wherein said neural network contains an arbitrary number N of the hidden layers, wherein each hidden layer contains an arbitrary number nj of neurons, wherein each node i of the input layer outputs its value to all neurons j in a subsequent hidden layer, and wherein each neuron j of said hidden layer outputs its value to all neurons j in a subsequent hidden layer of said neural network;
b) calculating an output of neurons in each subsequent hidden layer of said neural network, wherein the output of each neuron j in each said hidden layer is calculated as a function fj (zj) of its inputs containing all the outputs of its preceding layer, wherein the function zj is a linear function of said inputs of a neuron j with different parameters for each neuron j, and wherein f (z) is a non-linear activation function; and
c) calculating an output of neurons in an output layer, wherein the output of each neuron j in the output layer is calculated as a function fs(z) of its inputs containing all outputs of its preceding hidden N-layer, wherein the function zj is a linear function of inputs of a neuron j with different parameters for each neuron j, wherein fs(z) is a non-linear activation function of the output neuron, and wherein low and high activation levels of each neuron are associated with output labels: fs(z) larger than a threshold value and fs(z) less than a threshold value, said two labels correspond to the output values of ‘1’ and ‘0’, respectively.