Magnetic Field Extrapolation Based on Improved Back Propagation Neural Network

• Published in: Artificial Intelligence and Computational Intelligence pp. 64 - 70
• Main Authors: Lian, Li-ting, Xiao, Chang-han, Liu, Sheng-dao, Zhou, Guo-hua, Yang, Ming-ming
• Format: Book Chapter
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2010
• Series: Lecture Notes in Computer Science
• Subjects: magnetic field extrapolation; PSO; underwater field; upper air field
• ISBN: 9783642165290; 364216529X
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-642-16530-6_9

Magnetic anomaly created by ferromagnetic ships may make them vulnerable to detections and mines. In order to reduce the anomaly, it is important to evaluate magnetic field firstly. Underwater field can be measured easily, but upper air field is hard to be got. To achieve it, a model able to predict upper air magnetic field from underwater measurements is required. In this paper, a Back Propagation (BP) model has been built and it can escape from local optimum thanks to optimizing the initial weights and threshold values by Particle Swarm Optimization (PSO) algorithm. The method can avoid many problems from linear model and its high accuracy and good robustness have been tested by a mockup experiment.