A proposed self-organizing radial basis function network for aero-engine thrust estimation

Abstract

This paper proposes a new algorithm to construct self-organizing radial basis function neural networks (RBFNNs) for aero-engine thrust estimation. The algorithm can not only optimize centers and network size of the RBFNN but also automatically determine the connection weights. To reduce the dimensionality of particle and speed up the optimization process, spreads of an RBFNN are randomly initialized. Its weights are dynamically derived and adjusted by the product between the Moore–Penrose inverse of the hidden layer's outputs and the desired outputs. To optimize the centers and network size of the RBFNN, a strategy named multi-Gbest is adopted. Based on all these strategies, the proposed algorithm can effectively generate self-organizing RBFNNs with high accuracy. The successful application to aero-engine thrust estimation shows the practicability and effectiveness of the proposed algorithm.