MELM-GRBFNN: A modified Extreme Learning Machine trained Gaussian Radial Basis Function Neural Network model for estimating blocking probability of OBS Network

Neural networks are extensively used for determining different characteristics of optical burst switching networks. The main disadvantage of optical burst switching network is burst drop and burst contention, which occurs because of burst getting blocked. Using neural network approaches, blocking probability can be pre-determined for the upcoming traffic. In this paper, Log-incremental modified extreme learning machine trained generalized radial basis function neural network (MELM-GRBFNN) model is used for training and predicting burst contention or burst blocking probability. From the obtained results, it is inferred that the prediction accuracy of our proposed model is more accurate and faster than the contemporary approaches. It is observed that our proposed method is competent in predicting the burst blocking probability with higher accuracy and indicates a reduction in the burst loss. Thus, it will help network designers to have a preliminary idea about the performance of the network model under specific configurations.