Initialized RHPNN for Fault Detection in MEMS
نویسندگان
چکیده
-Micro Electro Mechanical Systems will soon usher in a new technological renaissance. Just as ICs brought the pocket calculator, PC, and video games, MEMS will provide a new set of products and markets. Learn about the state of the art, from inertial sensors to microfluidic devices[1]. Over the last few years, considerable effort has gone into the study of the failure mechanisms and reliability of MEMS. Although still very incomplete, our knowledge of the reliability issues relevant to MEMS is growing. One of the major problems in MEMS production is fault detection. After fault diagnosis, hardware or software methodscan be used to overcome it. Most of MEMS have nonlinear and complex models. So it is difficult or impossible to detect the faults by traditional methods, which are model-based. In this paper an initialized Robust Heteroscedastic Probabilistic Neural Network is used for fault detection in a RF MEMS. Key-Words: Fault detection, intelligent method, Neural Networks, MEMS.
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