The application of FAOS-PFNN in biomedical signal processing
Every doctor needs to learn how to diagnose accurately and reliably. Based on observations and knowledge, they have to diagnose illnesses and give individual treatment to each patient. Although there are numerous medical books, records and courses assisting doctors with their de...
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Format: | Theses and Dissertations |
Language: | English |
Published: |
2013
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Online Access: | http://hdl.handle.net/10356/54689 |
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Institution: | Nanyang Technological University |
Language: | English |
Summary: | Every doctor needs to learn how to diagnose accurately and reliably. Based on
observations and knowledge, they have to diagnose illnesses and give individual
treatment to each patient. Although there are numerous medical books, records and
courses assisting doctors with their deduction, the medical knowledge outdates
quickly and cannot replace one's own experience. To handle this challenge, this
dissertation makes a thorough research and comparison among several fuzzy neural
networks (FNNs), selects the fast and accurate online self-organizing scheme for
parsimonious fuzzy neural networks (FAOS-PFNN) and successfully applies it to
biomedical diagnosis and prediction. Unlike other fuzzy neural networks,
FAOS-PFNN is a more practical method which does not require structure
identification in advance and can achieve a more compact network structure. In this
dissertation, the programming of FAOS-PFNN is realized using MATLAB.
Simulation results indicate that the FAOS-PFNN algorithm can reasonably
approximate nonlinear functions and find out complex relationships in the data,
efficiently boost the objectivity and accuracy of diagnosis and therefore improve the
computer assisted medical diagnosis. |
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