The application of FAOS-PFNN in biomedical signal processing

• Main Author: Chen, Yuting.
• Other Authors: Er Meng Joo
• Format: Theses and Dissertations
• Language: English
• Published: 2013
• Subjects: DRNTU::Engineering::Electrical and electronic engineering
• Online Access: http://hdl.handle.net/10356/54689
• Institution: Nanyang Technological University

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.