Electronic Information Signal Recognition Based on a Stochastic Neural Network Algorithm

• Published in: Journal of control science and engineering Vol. 2022; pp. 1 - 8
• Main Author: Wang, Jiaye
• Format: Journal Article
• Language: English
• Published: New York Hindawi 31.07.2022; John Wiley & Sons, Inc; Wiley
• Subjects: Accuracy; Algorithms; Emergency communications systems; Errors; Feature extraction; Fiber optics; Intrusion; Neural networks; Optical communication; Optical fibers; Recognition; Singular value decomposition; Training; Vibration
• ISSN: 1687-5249; 1687-5257; 1687-5257
• DOI: 10.1155/2022/6473392

In order to improve the recognition accuracy of SCN for optical fiber data, a method of optical fiber intrusion signal recognition based on SCN (TSVD-SCN) based on truncated singular value decomposition (TSVD) is proposed in this paper. TSVD-SCN performs SVD decomposition on the hidden layer output of the network and sets a threshold to remove the smaller singular values, so as to reduce the number of conditions of the hidden layer output matrix and improve the network recognition rate. This paper uses the method of duty cycle, average amplitude difference function, and FFT to calculate the energy duty cycle for feature extraction and uses TSVD-SCN algorithm to classify and recognize different intrusion vibration feature vectors. The experimental results show that the root mean square errors of TSVD-SCN and SCN networks are significantly less than RVFL. After the hidden layer node L=20, the training error decline speed of RVFL tends to be gentle. When LRVFL=Lmax, the learning effect is the best, and RMSERVFL=0.3. With the continuous increase of L, the training error of SCN network and TSVD-SCN network will be reduced to very small, and the training error of TSVD-SCN network is also less than SCN. Conclusion. The accuracy of the algorithm model proposed in this paper is higher than that of the SCN model. It can accurately identify the types of optical fiber intrusion signals, which is of great significance to improve the classification accuracy of the SCN network in practical applications.