TY - JOUR
T1 - The clustering algorithm for nonlinear system identification
AU - Rubio Avila, José De Jesús
AU - Ramírez, Andrés Ferreyra
AU - Avilés-Cruz, Carlos
AU - Vazquez-Alvarez, Ivan
PY - 2008
Y1 - 2008
N2 - A new on-line clustering fuzzy neural network is proposed. In the algorithm, structure and parameter learning are updated at the same time. There is not difference between structure learning and parameter learning. It generates groups with a given radius. The center is updated in order to get that the center is near to the incoming data in each iteration, in this way. It does not need to generate a new rule in each iteration, i.e., it does not generate many rules and it does not need to prune the rules.
AB - A new on-line clustering fuzzy neural network is proposed. In the algorithm, structure and parameter learning are updated at the same time. There is not difference between structure learning and parameter learning. It generates groups with a given radius. The center is updated in order to get that the center is near to the incoming data in each iteration, in this way. It does not need to generate a new rule in each iteration, i.e., it does not generate many rules and it does not need to prune the rules.
KW - Clustering algorithm
KW - Fuzzy systems
KW - Identification
KW - Modeling
UR - http://www.scopus.com/inward/record.url?scp=59249105556&partnerID=8YFLogxK
M3 - Artículo
SN - 1109-2750
VL - 7
SP - 1179
EP - 1188
JO - WSEAS Transactions on Computers
JF - WSEAS Transactions on Computers
IS - 8
ER -