Pole -mounted sonar vibration prediction using CMAC neural networks

<p>The efficiency and accuracy of pole-mounted sonar systems are severely affected by pole vibration, Traditional signal processing techniques are not appropriate for the pole vibration problem due to the nonlinearity of the pole vibration and the lack of a priori knowledge about the statistics of the data to be processed. A novel approach of predicting the pole-mounted sonar vibration using CMAC neural networks is presented. The feasibility of this approach is studied in theory, evaluated by simulation and verified with a real-time laboratory prototype, Analytical bounds of the learning rate of a CMAC neural network are derived which guarantee convergence of the weight vector in the mean. Both simulation and experimental results indicate the CMAC neural network is an effective tool for this vibration prediction problem.</p>

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

Dissertation

Year

2005

Author dc:creator

• Zhang, Chunshu

Contributors dc:contributor

• L Gordon Kraft

• Engineering
• Electronics and Electrical
• Artificial Intelligence

Repository record dc:identifier

https://scholars.unh.edu/dissertation/280

OAI identifier oai:identifier

oai:scholars.unh.edu:dissertation-1279