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

Claim 15:
16. The method of claim 14, wherein said DL method further comprises:
a) feeding the input string into a neural network, wherein the 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 each node of the neural network outputs its value to all nodes in a first hidden layer of said neural network;
b) calculating an output of neurons in said first hidden layer, wherein the output of each neuron j in the first hidden layer is calculated as a function fj(zj) of its inputs containing all the outputs of the input 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;
c) calculating an output of neurons in a second hidden layer, wherein the output of each neuron j in said second hidden layer is calculated as a function fj(zj) of its inputs containing all the outputs of the first hidden 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
d) calculating an output of neurons in an output fourth layer, wherein the output of each neuron j in the output layer is calculated as a function fs(z) of its inputs containing all the outputs of the second hidden layer, wherein the function zj is a linear function of said 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 two output frequencies or amplitudes fs(z) >0.5 and fs(z)<0.5, said two frequencies or amplitudes correspond to the output values of ‘1’ and ‘0’, respectively.