TY - GEN
T1 - Study on machine vision fuzzy recognition based on matching degree of multi-characteristics
AU - Lei, Jingtao
AU - Wang, Tianmiao
AU - Gong, Zhenbang
PY - 2010
Y1 - 2010
N2 - This paper presents a new method used for fruit category recognition based on machine vision and total matching degree of fruit's multi-characteristics. The ladder membership function was used to express each characteristic. The matching degree of each characteristic was calculated by its membership function, and then the total matching degree was calculated, fruit category recognition can be determined by the total matching degree. In this paper, a 5-input 1-output zero-order Takagi-Sugeno fuzzy neural network was constructed to achieve non-linear mapping between fruit characteristics and fruit type, then the parameters of membership function for each characteristic was designed as learning parameters of the network. Training the fuzzy neural network through a large amount of sample data, the corresponding parameters of the membership functions of recognized fruit can be determined. Taking apple recognition as an example, the experimental results show that the method is simple, effective, highly precise, easy to implement.
AB - This paper presents a new method used for fruit category recognition based on machine vision and total matching degree of fruit's multi-characteristics. The ladder membership function was used to express each characteristic. The matching degree of each characteristic was calculated by its membership function, and then the total matching degree was calculated, fruit category recognition can be determined by the total matching degree. In this paper, a 5-input 1-output zero-order Takagi-Sugeno fuzzy neural network was constructed to achieve non-linear mapping between fruit characteristics and fruit type, then the parameters of membership function for each characteristic was designed as learning parameters of the network. Training the fuzzy neural network through a large amount of sample data, the corresponding parameters of the membership functions of recognized fruit can be determined. Taking apple recognition as an example, the experimental results show that the method is simple, effective, highly precise, easy to implement.
KW - Fuzzy neural network
KW - Fuzzy recognition
KW - Matching degree
KW - Membership functions
KW - Multi-characteristic
UR - https://www.scopus.com/pages/publications/78649609546
U2 - 10.1007/978-3-642-15615-1_54
DO - 10.1007/978-3-642-15615-1_54
M3 - 会议稿件
AN - SCOPUS:78649609546
SN - 3642156142
SN - 9783642156144
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 459
EP - 468
BT - Life System Modeling and Intelligent Computing - Int. Conf. on Life System Modeling and Simulation, LSMS 2010 and Int. Conf. on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2010
T2 - 2010 International Conference on Life System Modeling and Simulation, LSMS 2010 and the 2010 International Conference on Intelligent Computing for Sustainable, Energy and Environment, ICSEE 2010
Y2 - 17 September 2010 through 20 September 2010
ER -