Optical fiber intrusion signal recognition method based on TSVD-SCN

Abstract

Abstract Stochastic Configuration Networks (SCN) introduce the inequality constraint of a supervised mechanism to ensure the universal approximation property of learner model. However, in the processing of building SCN, due to the properties of used activation function and the way of assigning the random input weights and biases of the hidden nodes, the hidden output matrix is often ill-posed, i.e., the matrix can be of rank deficient or demonstrate multicollinearity. Thus, the least squares method for evaluating the output weights may result in poor generalization performance for data modelling problems. This paper aims to overcome this drawback through modifying the computation of the generalized pseudo inverse of the output matrix by a Truncated Singular Value Decomposition (TSVD) method with an adaptively chosen truncation threshold. The improved SCN model is then applied for recognizing intrusion signals in Optical Fiber Pre-warning System. Experimental results show that the proposed improved algorithm can achieve higher recognition rate compared to the original SCN classifier.