Hello, I can help.

I have developed a simple pseudo code for ANN in MATLAB. The use, though depends on the problem at hand:

// Define network parameters

input_layer_size = number of input features

hidden_layer_size = number of neurons in hidden layer

num_labels = number of output classes

// Initialize weights randomly

Theta1 = initialize_weights(input_layer_size, hidden_layer_size)

Theta2 = initialize_weights(hidden_layer_size, num_labels)

// Combine weights into a single vector for optimization

nn_params = [Theta1(:); Theta2(:)]

// Define training data

X = input data matrix

y = labels vector

// Optimization setup

max_iterations = desired number of optimization iterations

lambda = regularization parameter

// Cost function and backpropagation

function costAndGrad = nnCostFunction(nn_params, input_layer_size, hidden_layer_size, num_labels, X, y, lambda)

// Reshape parameters back to matrices

Theta1 = reshape(nn_params(1:hidden_layer_size * (input_layer_size + 1)), hidden_layer_size, input_layer_size + 1)

Theta2 = reshape(nn_params(hidden_layer_size * (input_layer_size + 1) + 1:end), num_labels, hidden_layer_size + 1)

// Forward propagation

a1 = [ones(m, 1) X] // Add bias unit to input layer

z2 = a1 * Theta1'

a2 = sigmoid(z2)

a2 = [ones(size(a2, 1), 1) a2] // Add bias unit to hidden layer

z3 = a2 * Theta2'

a3 = sigmoid(z3)

// Compute cost

J = compute_cost(a3, y, Theta1, Theta2, lambda)

// Backpropagation

Delta1 = 0

Delta2 = 0

for each training example

delta3 = a3 - y_one_hot

delta2 = (delta3 * Theta2(:, 2:end)) .* sigmoidGradient(z2)

Delta2 = Delta2 + delta3' * a2

Delta1 = Delta1 + delta2' * a1

end

// Compute gradients with regularization

Theta1_grad = (1/m) * Delta1 + (lambda/m) * [zeros(size(Theta1, 1), 1) Theta1(:, 2:end)]

Theta2_grad = (1/m) * Delta2 + (lambda/m) * [zeros(size(Theta2, 1), 1) Theta2(:, 2:end)]

// Unroll gradients

grad = [Theta1_grad(:); Theta2_grad(:)]

return J, grad

end

// Use an optimizer to minimize the cost

optimized_params = optimize('fmincg', nnCostFunction, nn_params, max_iterations, lambda, X, y)

// Reshape optimized parameters

Theta1 = reshape(optimized_params(1:hidden_layer_size * (input_layer_size + 1)), hidden_layer_size, input_layer_size + 1)

Theta2 = reshape(optimized_params(hidden_layer_size * (input_layer_size + 1) + 1:end), num_labels, hidden_layer_size + 1)

// Prediction function for making predictions with trained network

function predictions = predict(Theta1, Theta2, X)

// Forward propagate using the trained weights

a1 = [ones(size(X, 1), 1) X]

z2 = a1 * Theta1'

a2 = sigmoid(z2)

a2 = [ones(size(a2, 1), 1) a2]

z3 = a2 * Theta2'

a3 = sigmoid(z3)

// Return the class with highest probability

predictions = argmax(a3, 2)

end

// Use the network to predict on new data or test data

Would like to get ask and get more information

Yes, I would like to know more about your question

I have already published a paper using ANN of matlab