PMID- 19731399
OWN - NLM
STAT- MEDLINE
DCOM- 20091006
LR  - 20191210
IS  - 0129-0657 (Print)
IS  - 0129-0657 (Linking)
VI  - 19
IP  - 4
DP  - 2009 Aug
TI  - A fully complex-valued radial basis function network and its learning algorithm.
PG  - 253-67
AB  - In this paper, a fully complex-valued radial basis function (FC-RBF) network with 
      a fully complex-valued activation function has been proposed, and its 
      complex-valued gradient descent learning algorithm has been developed. The fully 
      complex activation function, sech(.) of the proposed network, satisfies all the 
      properties needed for a complex-valued activation function and has Gaussian-like 
      characteristics. It maps C(n) --> C, unlike the existing activation functions of 
      complex-valued RBF network that maps C(n) --> R. Since the performance of the 
      complex-RBF network depends on the number of neurons and initialization of 
      network parameters, we propose a K-means clustering based neuron selection and 
      center initialization scheme. First, we present a study on convergence using 
      complex XOR problem. Next, we present a synthetic function approximation problem 
      and the two-spiral classification problem. Finally, we present the results for 
      two practical applications, viz., a non-minimum phase equalization and an 
      adaptive beam-forming problem. The performance of the network was compared with 
      other well-known complex-valued RBF networks available in literature, viz., 
      split-complex CRBF, CMRAN and the CELM. The results indicate that the proposed 
      fully complex-valued network has better convergence, approximation and 
      classification ability.
FAU - Savitha, R
AU  - Savitha R
AD  - School of Electrical and Electronics Engineering, Nanyang Technological 
      University, Singapore.
FAU - Suresh, S
AU  - Suresh S
FAU - Sundararajan, N
AU  - Sundararajan N
LA  - eng
PT  - Journal Article
PL  - Singapore
TA  - Int J Neural Syst
JT  - International journal of neural systems
JID - 9100527
SB  - IM
MH  - *Algorithms
MH  - *Artificial Intelligence
MH  - Humans
MH  - *Neural Networks, Computer
MH  - Signal Processing, Computer-Assisted
EDAT- 2009/09/05 06:00
MHDA- 2009/10/07 06:00
CRDT- 2009/09/05 06:00
PHST- 2009/09/05 06:00 [entrez]
PHST- 2009/09/05 06:00 [pubmed]
PHST- 2009/10/07 06:00 [medline]
AID - S0129065709002026 [pii]
AID - 10.1142/S0129065709002026 [doi]
PST - ppublish
SO  - Int J Neural Syst. 2009 Aug;19(4):253-67. doi: 10.1142/S0129065709002026.