This paper presents the rule optimization, tuning of the membership functions, and optimization of the number of fuzzy rules, of a neural-fuzzy network (NFN) using a genetic algorithm (GA). The objectives are achieved by training a proposed NFN with rule switches. The proposed NFN and GA are employed to interpret graffiti number inputs and commands for electronic books (eBooks).
- Electronic books (eBooks)
- Genetic algorithm (GA)
- Neural-fuzzy networks (NFNs)
ASJC Scopus subject areas
- Electrical and Electronic Engineering