# One-dimensional regression network

2 ビュー (過去 30 日間)
Fernando Meneses 2021 年 8 月 23 日
Hello!
I want to create a one-dimensional regression network that can predict certain parameters in a function. Currently I don't know which is the best approach to solve my problem nor the network architecture that I should use.
I describe my problem with a simple example. Let's say I have several sets of sinusoidal functions [A*sin(wt+t0)], each set with well defined parameters: amplitude [A] of the signal and angular frequency [w]. However, the starting point in each function [t0] is set randomly.
Set 1, stored in matrix M1, with 10 samples:
NSamples1 = 10;
t = 0:100; % Time [s]
w1 = 10; % Angular frequency [rad/s]
A1 = 1; % Amplitude
M1 = zeros(NSamples1,numel(t)); % Preallocation
for k = 1:NSamples1
M1(k,:) = A1*sin(w1*t+rand(1)*2*pi/w1); % Define all the samples, with random starting points
end
plot(t,M1)
title('Samples for Set 1')
I repeat the same procedure for a second set:
Set 2, stored in matrix M2, with 10 samples:
NSamples2 = 10;
t = 0:100; % Time [s]
w2 = 15; % Angular frequency [rad/s]
A2 = 2; % Amplitude
M2 = zeros(NSamples2,numel(t)); % Preallocation
for k = 1:NSamples2
M2(k,:) = A2*sin(w2*t+rand(1)*2*pi/w2); % Define all the samples, with random starting points
end
plot(t,M2)
title('Samples for Set 2')
Imagine I continue the process up to N sets.
Goal:
1) Train the network with all these sets.
2) Feed it with a new sinusoidal function [A'*sin(w'*t)+x0'], in which the parameters A' and w' are similar to the ones that I used in the different sets, but not necessarily equals.
3) Predict the parameters A' and w'.
Thank you very much!!!

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