Structurally adaptive neural network for underwater target classification

Qiang Huang; Mahmood R. Azimi-Sadjadi; Sassan Sheedvash

doi:10.1117/12.324206

4 September 1998 Structurally adaptive neural network for underwater target classification

Qiang Huang, Mahmood R. Azimi-Sadjadi, Sassan Sheedvash

Proceedings Volume 3392, Detection and Remediation Technologies for Mines and Minelike Targets III; (1998) https://doi.org/10.1117/12.324206
Event: Aerospace/Defense Sensing and Controls, 1998, Orlando, FL, United States

Abstract

This paper presents the application of a novel scheme for dynamic structural adaptation for back-propagation neural networks. It utilizes the time and order update formulations in the orthogonal projection theorem to establish a recursive weight updating procedure for the training process and a dynamic node creation procedure during the training process. The effectiveness of the algorithm is demonstrated on a simple multiplexer problem and a real-life application dealing with underwater target classification from the acoustic backscattered signals. It is shown through the simulation results that the dynamic structural adaptation scheme offers better trainability for the networks without requiring prohibitive cost of retraining. In addition, the results on the testing data indicate good classification performance of the network trained in conjunction with the structural adaptation method.

Citation Download Citation

Qiang Huang, Mahmood R. Azimi-Sadjadi, and Sassan Sheedvash "Structurally adaptive neural network for underwater target classification", Proc. SPIE 3392, Detection and Remediation Technologies for Mines and Minelike Targets III, (4 September 1998); https://doi.org/10.1117/12.324206

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