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This letter aims at determining the optimal bias and magnitude of initial weight vectors based on multidimensional geometry. This method ensures the outputs of neurons are in the active region and the range of the activation function is fully utilized. In this letter, very thorough simulations and comparative study were performed to validate the performance(More)
An algorithm for determining the optimal initial weights of feedforward neural networks based on the Cauchy's inequality and a linear algebraic method is developed. The algorithm is computational e$cient. The proposed method ensures that the outputs of neurons are in the active region and increases the rate of convergence. With the optimal initial weights(More)
An extended least squares-based algorithm for feedforward networks is proposed. The weights connecting the last hidden and output layers are first evaluated by least squares algorithm. The weights between input and hidden layers are then evaluated using the modified gradient descent algorithms. This arrangement eliminates the stalling problem experienced by(More)
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