C-loss based extreme learning machine for estimating power of small-scale turbojet engine

Abstract

As a high-efficiency training method for single layer feedforward neural networks, extreme learning machine (ELM) has drawn much interest recently, but its robustness is not good due to the adoption of the square loss function. Hence, the convex loss function in ELM is replaced with a nonconvex loss function, i.e., the C-loss function, so a novel algorithm, called C-loss based ELM (CELM), is proposed in this paper. According to the experimental results on a toy example and two benchmark data sets, CELM performs better than the other algorithms with respect to the generalization performance. To be more important, when CELM is used to estimate power of small-scale turbojet engine, it also dominates the other algorithms, which makes the development of the potential control structure, viz., the direct power control, for the unmanned aerial vehicles more feasible.