Page 146 - Artificial Intelligence in the Age of Neural Networks and Brain Computing
P. 146
References 135
[76] F.L. Minku, T.B. Ludermir, Evolutionary strategies and genetic algorithms for dy-
namic parameter optimization of evolving fuzzy neural networks, in: Proc. of the
IEEE Congress on Evolutionary Computation, Scotland, 2005, 2005, pp. 1951e1958.
[77] K. Yamauchi, J. Hayami, Incremental leaning and model selection for radial basis
function network through sleep, IEICE Transactions on Information and Systems
e90-d (4) (2007) 722e735.
[78] D.F. Leite, P. Costa, F. Gomide, Interval-based evolving modeling, in: Proc. of the
IEEE Workshop on Evolving and Self-Developing Intelligent Systems, USA, March
2009, 2009, pp. 1e8.
[79] D.F. Leite, P. Costa, F. Gomide, Evolving granular neural networks from fuzzy data
streams, Neural Networks 38 (2013) 1e16.
[80] J. de Jesu ´s Rubio, Stability analysis for an online evolving neuro-fuzzy recurrent
network, in: P.P. Angelov, D.P. Filev, N.K. Kasabov (Eds.), Evolving Intelligent Sys-
tems: Methodology and Applications, John Wiley & Sons, Hoboken, New Jersey,
USA, 2010.
[81] K. Kim, E.J. Whang, C.W. Park, E. Kim, M. Park, ATSK fuzzy inference algorithm for
online identification, in: Proc. of the 2nd Intl Conf. on Fuzzy Systems and Knowledge
Discovery, China, August 2005, 2005, pp. 179e188.
[82] C. Zanchettin, L.L. Minku, T.B. Ludermir, Design of experiments in neuro-fuzzy
systems, International Journal of Computational Intelligence and Applications 9 (2)
(2010) 137e152.
[83] F.L. Minku, T.B. Ludermir, EFuNNs ensembles construction using a clustering method
and a coevolutionary genetic algorithm, in: Proc. of the IEEE Congress on Evolu-
tionary Computation, Canada, July 2006, 2006, pp. 1399e1406.
[84] S.W. Tung, C. Quek, C. Guan, eT2FIS: an evolving Type-2 neural fuzzy inference
system, Information Sciences 220 (2013) 124e148.
[85] B. O’Hara, J. Perera, A. Brabazon, Designing radial basis function networks for clas-
sification using differential evolution, in: Proc. of the Int’l Joint Conf. on Neural Net-
works, Canada, July 2006, 2006, pp. 2932e2937.
[86] K. Subramanian, S. Sundaram, N. Sundararajan, A metacognitive neuro-fuzzy infer-
ence system (McFIS) for sequential classification problems, IEEE Transactions on
Fuzzy Systems 21 (6) (2013) 1080e1095.
[87] J.A.M. Herna ´ndez, F.G. Castan ˜eda, J.A.M. Cadenas, An evolving fuzzy neural
network based on the mapping of similarities, IEEE Transactions on Fuzzy Systems
17 (6) (2009) 1379e1396.
[88] Q.L. Zhao, Y.H. Jiang, M. Xu, Incremental learning by heterogeneous bagging
ensemble, in: Proc. of the Int’l Conf. on Advanced Data Mining and Applications,
China, November 2010, 2010, pp. 1e12.
[89] H. Goh, J.H. Lim, C. Quek, Fuzzy associative conjuncted maps network, IEEE Trans-
actions on Neural Networks 20 (8) (2009) 1302e1319.
[90] F.L. Minku, T.B. Ludermir, EFuNN ensembles construction using CONE with multi-
objective GA, in: Proc. of the 9th Brazilian Symposium on Neural Networks, Brazil,
October 2006, 2006, pp. 48e53.
[91] N. Kasabov, Adaptation and interaction in dynamical systems: modelling and rule dis-
covery through evolving connectionist systems, Applied Soft Computing 6 (3) (2006)
307e322.