Gradient Descent optimization in an electrical circuit or transmission line
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Is there any sample work of gradient descent based optimization of an electrical circuit or transmission line parameters done using MATLAB? I am trying to optimize the parameters of a nonlinear transmission line using gradient descent algorithm. I intend to use Matlab and LTspice. Any help regarding this problem would be greatly appreciated. Thanks.
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nick
am 30 Sep. 2024
Hi Syed,
Here is a basic framework to get started for working with gradient descent optimization of an electrical cicuit :
Set Up MATLAB to Communicate with LTspice:
Use MATLAB to modify the LTspice netlist file to update the parameters. This can be done by reading and writing to the netlist file directly with the functions 'fopen' ,'fread', 'fwrite' and 'fclose' as shown:
function update_netlist_file(netlistFile, R_value)
% Read the netlist file
fid = fopen(netlistFile, 'r');
fileContent = fread(fid, '*char')';
fclose(fid);
% Update the params value, here assume Resistor (assume a line like .param R=...)
newContent =regexprep(fileContent, 'R=.*', sprintf('R=%.6f', R_value))
% Write the updated content back to the file
fid = fopen(netlistFile, 'w');
fwrite(fid, newContent);
fclose(fid);
end
function run_ltspice_simulation(ltspiceExe, netlistFile)
% Execute LTspice simulation
command = sprintf('"%s" -Run -b "%s"', ltspiceExe, netlistFile);
system(command);
pause(5); %Let the simulation be completed
end
function results = extract_simulation_results(rawFile)
% Use LTspice2Matlab or another method to read the .raw file
end
You can use 'LTspice2Matlab' a third party tool or can create your own parser to read the output file from LTspice. You can refer to the following documentation to learn more about these functions :
LTspice2Matlab: https://www.mathworks.com/matlabcentral/fileexchange/23394-fast-import-of-compressed-binary-raw-files-created-with-ltspice-circuit-simulator
Run LTspice Simulations from MATLAB:
Use MATLAB's system command to run LTspice simulations. You can execute LTspice from the command line using:
system('path_to_LTspiceXVII/XVIIx64.exe -Run -b netlist.cir');
After running the simulation, parse the output file (e.g., .raw or .log) to extract the results. You can refer to following documentation to learn more about 'system' function :
Implement Gradient Descent in MATLAB:
Use MATLAB's optimization toolbox or implement a custom gradient descent algorithm to minimize the error between the simulated and desired results.
Update the parameters in the LTspice netlist based on the gradient descent step.
Iterate Until Convergence:
Repeat the process of running simulations and updating parameters until the optimization converges to a satisfactory solution.
Hers's a skeletal MATLAB script that demonstrate the same
% Define initial parameters
params = [R_initial, L_initial, C_initial]; % Example parameters
learning_rate = 0.01;
tolerance = 1e-6;
max_iterations = 1000;
% Objective function to minimize
objective_function = @(params) run_simulation_and_get_error(params);
% Gradient descent loop
for iter = 1:max_iterations
% Calculate the current error
current_error = objective_function(params);
% Compute the gradient
gradient = compute_gradient(params, objective_function);
% Update parameters using gradient descent
params = params - learning_rate * gradient;
% Check for convergence
if norm(gradient) < tolerance
disp('Converged');
break;
end
fprintf('Iteration %d: Error = %.4f\n', iter, current_error);
end
% Final optimized parameters
disp('Optimized Parameters:');
disp(params);
% Function to run LTspice simulation and get error
function error = run_simulation_and_get_error(params)
% Parameters
R_value = params(1); % Example parameter to optimize
% File paths
netlistFile = 'path_to_your_netlist_file.net';
rawFile = 'path_to_your_output_file.raw';
ltspiceExe = 'path_to_LTspice/LTspice.exe';
% Update the netlist file with the new parameter
update_netlist_file(netlistFile);
% Run LTspice simulation
run_ltspice_simulation(ltspiceExe, netlistFile);
% Extract results from LTspice output
simulationResults = extract_simulation_results(rawFile);
% Compute the error between simulation results and desired results
desiredResults = get_desired_results(); % Define your desired results
error = compute_error(simulationResults, desiredResults);
end
function update_netlist_file(netlistFile, R_value)
% Read the netlist file
fid = fopen(netlistFile, 'r');
fileContent = fread(fid, '*char')';
fclose(fid);
% Update the params value, here assume Resistor (assume a line like .param R=...)
newContent =regexprep(fileContent, 'R=.*', sprintf('R=%.6f', R_value))
% Write the updated content back to the file
fid = fopen(netlistFile, 'w');
fwrite(fid, newContent);
fclose(fid);
end
function run_ltspice_simulation(ltspiceExe, netlistFile)
% Execute LTspice simulation
command = sprintf('"%s" -Run -b "%s"', ltspiceExe, netlistFile);
system(command);
pause(5); %Let the simulation be completed
end
function results = extract_simulation_results(rawFile)
% Use LTspice2Matlab or another method to read the .raw file
end
function error = compute_error(simulationResults, desiredResults)
% Mean squared error
error = mean((simulationResults - desiredResults).^2);
end
function desiredResults = get_desired_results()
% Define your desired results for comparison
% For example, a target voltage or current waveform
desiredResults = [1, 2, 3, 4, 5]; % Random data as a placeholder
end
function data = read_ltspice_raw(rawFile)
% You can use third-party tools like LTspice2Matlab or write your own parser
data = rand(1, 5); % Random data as a placeholder
end
% Function to compute gradient (finite differences as an example)
function grad = compute_gradient(params, objective_function)
delta = 1e-5;
grad = zeros(size(params));
for i = 1:length(params)
params_plus = params;
params_plus(i) = params_plus(i) + delta;
grad(i) = (objective_function(params_plus) - objective_function(params)) / delta;
end
end
You can refer to the following article on 'Running LTspice from MATLAB" to learn more about running netlists:
Hope this helps.
Zinea
am 30 Sep. 2024
To optimize the parameters of a nonlinear transmission line using ’Gradient Descent’, an integration of MATLAB for numerical optimization with LTSpice for circuit simulation can be used. Here are the steps that may be followed:
1.A MATLAB function to calculate the performance metric that needs to be optimized (e.g., minimizing signal distortion) is created as follows:
function cost = objectiveFunction(params)
% Simulate the circuit with given parameters
% params = [R, L, C, ...]; % Example parameters to optimize
% Use LTspice to simulate
% Extract the performance metric from the simulation results
% Example: cost = calculateSignalDistortion(results);
cost = ... % Define your cost calculation based on simulation results
end
2. The ‘Gradient Descent’ algorithm is set up to adjust the parameters iteratively as follows:
function optimizedParams = gradientDescent(initialParams, learningRate, numIterations)
params = initialParams;
for i = 1:numIterations
% Calculate the gradient (partial derivatives)
grad = computeGradient(@objectiveFunction, params);
% Update parameters based on gradient
params = params - learningRate * grad;
% Optionally, display progress
fprintf('Iteration %d: Cost = %.4f\n', i, objectiveFunction(params));
end
optimizedParams = params;
end
function grad = computeGradient(func, params)
% Numerical approximation of the gradient
epsilon = 1e-6;
grad = zeros(size(params));
for j = 1:length(params)
paramsPlus = params;
paramsPlus(j) = paramsPlus(j) + epsilon;
grad(j) = (func(paramsPlus) - func(params)) / epsilon;
end
end
3. LTSpice is used to simulate the circuit with given parameters. The following links may be referred:
- Sample MATLAB code to simulate in LTSpice for an R-L circuit: https://www.mathworks.com/matlabcentral/fileexchange/75713-link-matlab-to-ltspice
- Generate a netlist, simulate in LTSpice: https://www.mathworks.com/matlabcentral/fileexchange/75713-link-matlab-to-ltspice
4. The parameters are initialized, and the ‘Gradient Descent’ function is run to optimize the parameters:
initialParams = [1, 1e-3, 1e-9]; % Example initial values for R, L, C
learningRate = 0.01;
numIterations = 100;
optimizedParams = gradientDescent(initialParams, learningRate, numIterations);
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