Combining MLPs and RBFNNs to Detect Signals With Unknown Parameters

The detection of Gaussian signals with an unknown correlation coefficient rho_s is considered. Solutions based on neural networks (NNs) are studied, and a strategy for designing committee machines in a composite hypothesis test is proposed. A single multilayer perceptron (MLP) has been trained with rho_s uniformly varying in [0, 1]. Considering the decision boundaries for rho_s = 0 and rho_s = 1 and how an MLP approximates them, a detection scheme composed of two MLPs has been proposed. One of them MLP₁ has been trained with rho_s uniformly varying in [0, 0.5], and the other one MLP₂ has been trained with rho_s uniformly varying in [0.5, 1]. For making a decision, the higher output is compared to a threshold for each false-alarm probability (P _FA). This strategy simplifies the task of finding a compromise solution between the computational cost and the approximation error and outperforms the single-MLP detector. When MLP₁ is substituted with a radial basis function NN (RBFNN), a new combination strategy of the outputs is required. We propose separately thresholding the outputs and applying them to an or logic function. The performance of this detector is slightly better than the two-MLP one, and the computational cost is significantly reduced.