TY - GEN
T1 - Neural networks with node gates
AU - Myint, H. M.
AU - Murata, J.
AU - Nakazono, T.
AU - Hirasawa, K.
PY - 2000
Y1 - 2000
N2 - Function approximation problems for the ordinary neural networks may be rather difficult, if the function becomes complicated, due to the necessity of big network size and the possibilities of many local minima. A promissing way to solve these difficulties is the localization of the problem. According to this concept, a new architecture of neural network is proposed namely neural network with node gates. In this paper, a function approximation example is provided to demonstrate the better performance of the proposed network than the ordinary neural network.
AB - Function approximation problems for the ordinary neural networks may be rather difficult, if the function becomes complicated, due to the necessity of big network size and the possibilities of many local minima. A promissing way to solve these difficulties is the localization of the problem. According to this concept, a new architecture of neural network is proposed namely neural network with node gates. In this paper, a function approximation example is provided to demonstrate the better performance of the proposed network than the ordinary neural network.
UR - http://www.scopus.com/inward/record.url?scp=84881113226&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84881113226&partnerID=8YFLogxK
U2 - 10.1109/ROMAN.2000.892504
DO - 10.1109/ROMAN.2000.892504
M3 - Conference contribution
AN - SCOPUS:84881113226
SN - 078036273X
SN - 9780780362734
T3 - Proceedings - IEEE International Workshop on Robot and Human Interactive Communication
SP - 253
EP - 257
BT - Proceedings - 9th IEEE International Workshop on Robot and Human Interactive Communication, IEEE RO-MAN 2000
T2 - 9th IEEE International Workshop on Robot and Human Interactive Communication, IEEE RO-MAN 2000
Y2 - 27 September 2000 through 29 September 2000
ER -