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).
['K. F. Leung', 'Frank Hung-Fat Leung', 'H. K. Lam', 'Steve S. H. Ling']
On interpretation of graffiti digits and characters for eBooks: neural-fuzzy network and genetic algorithm approach