TY - GEN
T1 - Comparison of complex- and real-valued feedforward neural networks in their generalization ability
AU - Hirose, Akira
AU - Yoshida, Shotaro
PY - 2011/11/28
Y1 - 2011/11/28
N2 - We compare the generalization characteristics of complex-valued and real-valued feedforward neural networks when they deal with wave-related signals. We assume a task of function approximation. Experiments demonstrate that complex-valued neural networks show smaller generalization error than real-valued ones in particular when the signals have high degree of wave nature.
AB - We compare the generalization characteristics of complex-valued and real-valued feedforward neural networks when they deal with wave-related signals. We assume a task of function approximation. Experiments demonstrate that complex-valued neural networks show smaller generalization error than real-valued ones in particular when the signals have high degree of wave nature.
KW - Complex-valued neural network
KW - function approximation
KW - generalization
UR - http://www.scopus.com/inward/record.url?scp=81855190753&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=81855190753&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-24955-6_63
DO - 10.1007/978-3-642-24955-6_63
M3 - Conference contribution
AN - SCOPUS:81855190753
SN - 9783642249549
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 526
EP - 531
BT - Neural Information Processing - 18th International Conference, ICONIP 2011, Proceedings
T2 - 18th International Conference on Neural Information Processing, ICONIP 2011
Y2 - 13 November 2011 through 17 November 2011
ER -