This document presents the development of an algorithm that predicts the arrival of a secondary user (SU) to a base station (BS) in a cognitive network based on infrastructure, requesting a Best Effort (BE) or Real Time (RT) type of service with a determined bandwidth (BW) implementing neural networks.The algorithm dynamically uses a novel neural network construction technique using the geometric pyramid topology and trains a Multilayer Perceptron Neural Networks (MLPNN) based on the historical arrival of an SU to estimate future applications; This will allow to manage more quickly the information in the BS for the selection of the best channel in CRN as it is placed before the arrival of the SUs.As a final result the software application determines the probability of arrival at a future time point and calculates the performance metrics to measure the effectiveness of the predictions made.