Please use this identifier to cite or link to this item:
|Title:||Rule-based channel equalizer with learning capability||Authors:||Nie, Junhong
|Issue Date:||1995||Citation:||Nie, Junhong,Lee, T.H. (1995). Rule-based channel equalizer with learning capability. IEEE International Conference on Neural Networks - Conference Proceedings 1 : 606-611. ScholarBank@NUS Repository.||Abstract:||The problem of channel equalization is concerned with reconstructing binary signals being transmitted through a dispersive communication channel and then corrupted by additive noise. With the aid of fuzzy concepts and neural-like learning, this paper presents a rule-based approach to this problem. A self-organizing algorithm consisting of learning, pruning, and refining processes is developed aiming at building the rule-base from labeled observations. The rule-based equalizer makes the decision on the basis of measuring the similarity between the current observation and the obtained rule prototypes. The simulation studies on linear and nonlinear channels were used to demonstrate the performance of the proposed approach.||Source Title:||IEEE International Conference on Neural Networks - Conference Proceedings||URI:||http://scholarbank.nus.edu.sg/handle/10635/72898|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Feb 9, 2019
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.