added RC circuit w/ parasitic capacitance

*.ini

*.autosave

*.slxc

*.asv

*.mat

slprj

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% This script generates .mat files for various inputs on

% the RC model. The .mat files have a data variable

%  datRC with three columns:

% 

%       time  V_in    I_capacitor

% 

% Other variables like peak, freq etc are stored for

% relevant inputs.

duration = 1000;

resolution = 0.1;

N = duration / resolution;

t = 0:resolution:(duration-resolution);

noise = 25; % signal-to-noise ratio in dB

peak = 10;  % amplitude of signal

freq = 1;   % frequency of sine signal

bias = 3;   % bias of offset sine signal

% constant input

in = ones(N,1) * peak;

sim_in = [t', awgn(in, noise, 'measured')];

datRC = runSim('RC', sim_in);

save('resRC_const.mat', 'datRC', 'peak', 'noise');

% sine input

in = sin(2*pi*freq*t') * peak;

sim_in = [t', awgn(in, noise, 'measured')];

datRC = runSim('RC', sim_in);

save('resRC_sine.mat', 'datRC', 'freq', 'peak', 'noise');

% combined constant + sine

in = bias + sin(2*pi*freq*t') * peak;

sim_in = [t', awgn(in, noise, 'measured')];

datRC = runSim('RC', sim_in);

save('resRC_comb.mat', 'datRC', 'freq', 'peak', 'bias', 'noise');

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function results = runSim(model, sim_in)

% Runs a Simulink model. The `model` takes as input

% a workspace variable `sim_in` which is a matrix where

% the first column is a time stamp and the remaining

% columns are individual inputs. The model outputs to

% a workspace variable `sim_out` which is a timestamp

% object.

% The function parses the `sim_out` and returns `result`

% in the same form as `sim_in`.

stop_t = sim_in(end,1);

steps = diff(sim_in(:, 1));

minStep = min(steps(:));

set_param(model, 'StopTime', num2str(stop_t), 'MaxStep', num2str(minStep));

sim(model);

results = [sim_out.Time sim_out.Data];

end

# Data Driven Models

Using data to augment physical system models.

See [docs/](/docs/) for details.

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