Date of Award
Master of Applied Science
School of Information Technology and Mathematics
Faculty of Science, Technology and Engineering
Dr Hon Nien Cheung
Dr Jim Millar
An adaptive filter which can operate in an unknown environment by performing a learning mechanism that is suitable for the speech enhancement process. This research develops a novel ANN model which incorporates the fuzzy set approach and which can perform a non-linear function approximation. The model is used as the basic structure of an adaptive filter. The learning capability of ANN is expected to be able to reduce the development time and cost of the designing adaptive filters based on fuzzy set approach. A combination of both techniques may result in a learnable system that can tackle the vagueness problem of a changing environment where the adaptive filter operates. This proposed model is called Fuzzy Counterpropagation Network (Fuzzy CPN). It has fast learning capability and self-growing structure. This model is applied to non-linear function approximation, chaotic time series prediction and background noise elimination.
Wiryana, I. M. (1994). Applications of fuzzy counterpropagation neural networks to non-linear function approximation and background noise elimination. Retrieved from https://ro.ecu.edu.au/theses/1107