Abstract. This paper presents the adaptation of a single layer complex valued neural network (NN) to use entropy in the cost function instead of the usual mean squared error (MSE). This network has the good property of having only one layer so that there is no need to search for the number of hidden layer neurons: the topology is completely determined by the problem. We extend the existing stochastic MSE based learning algorithm to a batch MSE version first and then to a batch minimum error entropy (MEE). We present experiments showing the the proposed algorithms are competitive with other learning machines.
Alexandre REF presents the adaptation of a single layer CVNN using entropy in the cost function instead of the usual mean squared error (MSE).
Single Layer Complex Valued Neural Network with Entropic Cost Function
ICANN
Computer Science