Fig. 2
Schematic diagram showing the conceptual model of a multilayer perceptron with three-layers, and a summary of the fundamental equations used by applying the Levenberg–Marquardt algorithm: Eq. (1.1) error vector; Eq. (1.2) cost function, where W is the vector of all the weights and biases and q is the total number of samples or patters; Eq. (1.3) where j is the jth element of the gradient; Eq. (1.4) the gradient in matrix form; Eq. (1.5) Jacobian matrix, where p is the total number of neurons; Eq. (1.6) iterative update of weights using the LM algorithm, where wt+1 is the next weight, wt is the current weight, μ is the learning rate factor, and I is the identity matrix