Page 145 - Artificial Intelligence in the Age of Neural Networks and Brain Computing
P. 145
134 CHAPTER 6 Evolving and Spiking Connectionist Systems
[58] J. de Jesu ´s Rubio, SOFMLS: online self-organizing fuzzy modified least-squares
network, IEEE Transactions on Fuzzy Systems 17 (6) (2009) 1296e1309.
[59] G.B. Huang, N.Y. Liang, H.J. Rong, On-line sequential extreme learning machine, in:
Proc. of the IASTED Int’l Conf. on Computational Intelligence, Canada, July 2005,
2005, pp. 232e237.
[60] J.S. Lim, Finding features for real-time premature ventricular contraction detection us-
ing a fuzzy neural network system, IEEE Transactions on Neural Networks 20 (3)
(2009) 522e527.
[61] P.P. Angelov, X. Zhou, F. Klawonn, Evolving fuzzy rule-based classifiers, in: Proc. of
the IEEE Symposium on Computational Intelligence in Image and Signal Processing,
USA, April 2007, 2007, pp. 220e225.
[62] F. Liu, C. Quek, G.S. Ng, A novel generic Hebbian ordering-based fuzzy rule base
reduction approach to Mamdani neuro-fuzzy system, Neural Computation 19 (6)
(2007) 1656e1680.
[63] H. Song, C. Miao, W. Roel, Z. Shen, F. Catthoor, Implementation of fuzzy cognitive
maps based on fuzzy neural network and application in prediction of time series,
IEEE Transactions on Fuzzy Systems 18 (2) (2010) 233e250.
[64] J. de Jesu ´s Rubio, D.M. Va ´zquez, J. Pacheco, Backpropagation to train an evolving
radial basis function neural network, Evolving Systems 1 (3) (2010) 173e180.
[65] J.L. Aznarte, J.M. Benı ´tez, J.L. Castro, Smooth transition autoregressive models and
fuzzy rule-based systems: functional equivalence and consequences, Fuzzy Sets and
Systems 158 (24) (2007) 2734e2745.
[66] B. Cetisli, Development of an adaptive neuro-fuzzy classifier using linguistic hedges,
Expert Systems with Applications 37 (8) (2010) 6093e6101.
[67] K. Subramanian, S. Suresh, A meta-cognitive sequential learning algorithm for neuro-
fuzzy inference system, Applied Soft Computing 12 (11) (2012) 3603e3614.
[68] G.S. Babu, S. Suresh, Meta-cognitive RBF network and its projection based learning
algorithm for classification problems, Applied Soft Computing 13 (1) (2013)
654e666.
[69] S.W. Tung, C. Quek, C. Guan, SaFIN: a self-adaptive fuzzy inference network, IEEE
Transactions on Neural Networks 22 (12) (2011) 1928e1940.
[70] S. Suresh, K. Subramanian, A sequential learning algorithm for meta-cognitive neuro-
fuzzy inference system for classification problems, in: Proc. of the Int’l Joint Conf. on
Neural Networks, USA, August 2011, 2011, pp. 2507e2512.
[71] P. Kadlec, B. Gabrys, Architecture for development of adaptive on-line prediction
models, Memetic Computing 1 (4) (2009) 241e269.
[72] F.L. Minku, T.B. Ludemir, Clustering and co-evolution to construct neural network en-
sembles: an experimental study, Neural Networks 21 (9) (2008) 1363e1379.
[73] D.P. Filev, P.P. Angelov, Algorithms for real-time clustering and generation of rules
from data, in: J. Valente di Oliveira, W. Pedrycz (Eds.), Advances in Fuzzy Clustering
and its Applications, John Wiley & Sons, Chichester, UK, 2007.
[74] H. Amadou Boubacar, S. Lecoeuche, S. Maouche, SAKM: self-adaptive kernel ma-
chine: a kernel-based algorithm for online clustering, Neural Networks 21 (9)
(2008) 1287e1301.
[75] J. Tan, C. Quek, A BCM theory of meta-plasticity for online self-reorganizing fuzzy-
associative learning, IEEE Transactions on Neural Networks 21 (6) (2010) 985e1003.