Intrusion signal classification using stochastic configuration network with variable increments of hidden nodes

Abstract

In the environmental security monitoring application, an optical fiber prewarning system (OFPS) functions not only to locate the intrusion events but also recognize them. As a nonlinear network for recognition, the stochastic configuration network (SCN) is considered a promising method because it does not require setting the network scale beforehand. However, in the specific requirements of the application of OFPS, due to the small feature distance of different intrusion signals to be classified, it is necessary to set a smaller value of error tolerance. However, the side-effect is that meeting the constraint condition faces a challenge. To overcome this, we improve the configuration method of the hidden layer nodes in the SCN network. In the proceeding of the network process, the increment of the hidden layer nodes in each loop is gradually increased, and the space of the corresponding random parameters generated is enlarged. The SCN with variable increments of hidden nodes can adjust the number of hidden nodes added in each loop for continuous construction and obtaining higher classification accuracy. This study has a great significance for the application of SCN in the classification of intrusion signals in OFPS.