TY - GEN
T1 - An evolving neuro-fuzzy recurrent network
AU - Avila, José De Jesús Rubio
AU - Martínez, Jaime Pacheco
AU - Raḿrez, Andŕs Ferreyra
PY - 2009
Y1 - 2009
N2 - In this research, we propose an evolving neuro- fuzzy recurrent network (ENFRN). The network is capable to perceive the change in the actual system and adapt (self organize) itself to the new situation. The network generates a new hidden neuron if the smallest distance between the new data and all the existing hidden neurons (the winner neuron) is more than a given radius. We propose a new pruning algorithm based on the density. Density is the number of times each hidden neuron is used. If the value of the smallest density (the looser neuron) is smaller to a specified umbral, this neuron is pruned. We use a modied least square algorithm to train the parameters of the network. Structure and parameters learning are updated at the same time. The major contribution of this research is: we present the stability of the algorithm of the evolving neuro-fuzzy reccurrent network proposed. Two simulations give the effectiveness of the suggested algorithm.
AB - In this research, we propose an evolving neuro- fuzzy recurrent network (ENFRN). The network is capable to perceive the change in the actual system and adapt (self organize) itself to the new situation. The network generates a new hidden neuron if the smallest distance between the new data and all the existing hidden neurons (the winner neuron) is more than a given radius. We propose a new pruning algorithm based on the density. Density is the number of times each hidden neuron is used. If the value of the smallest density (the looser neuron) is smaller to a specified umbral, this neuron is pruned. We use a modied least square algorithm to train the parameters of the network. Structure and parameters learning are updated at the same time. The major contribution of this research is: we present the stability of the algorithm of the evolving neuro-fuzzy reccurrent network proposed. Two simulations give the effectiveness of the suggested algorithm.
UR - http://www.scopus.com/inward/record.url?scp=67650540750&partnerID=8YFLogxK
U2 - 10.1109/ESDIS.2009.4938993
DO - 10.1109/ESDIS.2009.4938993
M3 - Contribución a la conferencia
AN - SCOPUS:67650540750
SN - 9781424427543
T3 - 2009 IEEE Workshop on Evolving and Self-Developing Intelligent Systems, ESDIS 2009 - Proceedings
SP - 9
EP - 15
BT - 2009 IEEE Workshop on Evolving and Self-Developing Intelligent Systems, ESDIS 2009 - Proceedings
T2 - 2009 IEEE Workshop on Evolving and Self-Developing Intelligent Systems, ESDIS 2009
Y2 - 30 March 2009 through 2 April 2009
ER -