Fast learning network: a novel artificial neural network with a fast learning speed

Abstract

This paper proposes a novel artificial neural network called fast learning network (FLN). In FLN, input weights and hidden layer biases are randomly generated, and the weight values of the connection between the output layer and the input layer and the weight values connecting the output node and the input nodes are analytically determined based on least squares methods. In order to test the FLN validity, it is applied to nine regression applications, and experimental results show that, compared with support vector machine, back propagation, extreme learning machine, the FLN with much more compact networks can achieve very good generalization performance and stability at a very fast training speed and a quick reaction of the trained network to new observations. In addition, in order to further test the FLN validity, it is applied to model the thermal efficiency and NO x emissions of a 330 WM coal-fired boiler and achieves very good prediction precision and generalization ability at a high learning speed.