Feed Forward ANN Training using Back Propagation

Find a new weights by using Matlab code, when shown below is presented the input pattern (1 2 3) and the target output is 1. Using learning rate of 0.2 and bipolar sigmoid activation function, the bias is set to1.

Answer 1

[Shaik]

Hi,

Hope this resolves your issue

% Define the input pattern and target output
x = [1; 2; 3];
t = 1;

% Define the initial weights
w = randn(3, 1);

% Define the learning rate and bias
alpha = 0.2;
b = 1;

% Define the activation function
sigmoid = @(x) 2./(1+exp(-x)) - 1;

% Initialize the error and iteration counter
err = inf;
iter = 0;

% Loop until the error is small enough or a maximum number of iterations is reached
while err > 0.01 && iter < 1000
    % Calculate the net input
    net = w' * x + b;
    
    % Calculate the output of the neuron using the bipolar sigmoid activation function
    y = sigmoid(net);
    
    % Calculate the error
    e = t - y;
    
    % Update the weights and bias
    dw = alpha * e * x;
    w = w + dw;
    db = alpha * e;
    b = b + db;
    
    % Calculate the squared error
    err = e^2;
    
    % Increment the iteration counter
    iter = iter + 1;
end

% Display the results
disp(['Final weights: ' num2str(w')]);

Final weights: 1.4659    -0.55741      1.9576

disp(['Final bias: ' num2str(b)]);

Final bias: 1.0003

disp(['Final output: ' num2str(y)]);

Final output: 0.99854

[John D'Errico]

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[Warren]

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