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132 CHAPTER 6 Evolving and Spiking Connectionist Systems

[18] T. Kohonen, Self-Organising Maps, Springer, 1992.
[19] Carpenter, Grossberg, Adaptive Resonance Theory, 1994, MIT Press.
[20] R.S. Sutton, G. Andrew, Barto: Reinforcement Learning, MIT Press, 1998.
[21] C.M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
[22] N.K. Kasabov, Foundations of Neural Networks, Fuzzy Systems and Knowledge
Engineering, MIT Press, Cambridge, Massachusetts, USA, 1996.
[23] N.K. Kasabov, S.I. Shishkov, A connectionist production system with partial match
and its use for approximate reasoning, Connection Science 5 (3e4) (1993) 275e305.
[24] T. Yamakawa, E. Uchino, T. Miki, H. Kusanagi, A neo fuzzy neuron and its application
to system identification and prediction of the system behavior, in: Proc. 2nd Int. Conf.
on Fuzzy Logic and Neural Networks, Japan, July 1992, 1992, pp. 477e483.
[25] T. Furuhashi, T. Hasegawa, S. Horikawa, Y. Uchikawa, An adaptive fuzzy controller
using fuzzy neural networks, in: Proc. of 5th Int’l Fuzzy System Association World
Congress, Korea, July 1993, 1993, pp. 769e772.
[26] N.K. Kasabov, J.S. Kim, M.J. Watts, A.R. Gray, FuNN/2 e a fuzzy neural network
architecture for adaptive learning and knowledge acquisition, Information Sciences e
Applications 101 (3e4) (1997) 155e175.
[27] C. Darwin, On the Origin of Species by Means of Natural Selection, or the Preservation
of Favoured Races in the Struggle for Life, first ed., John Murray, London, 1859, p. 502.
Retrieved March 1, 2011.
[28] C. Darwin, On the Origin of Species by Means of Natural Selection, or the Preserva-
tion of Favoured Races in the Struggle for Life, second ed., John Murray, London,
1860. Retrieved January 9, 2009.
[29] T. Ba ¨ck, D.B. Fogel, Z. Michalewicz (Eds.), Handbook of Evolutionary Computation,
1997. ISBN: 0750303921.
[30] D.E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning,
Addison Wesley, 1989.
[31] M. Defoin-Platel, S. Schliebs, N.K. Kasabov, Quantum-inspired evolutionary algo-
rithm: a multimodel EDA, IEEE Transactions on Evolutionary Computation 13 (6)
(2009) 1218e1232.
[32] H.N.A. Hamed, N.K. Kasabov, S. Shamsuddin, Probabilistic evolving spiking neural
network optimization using dynamic quantum inspired particle swarm optimization,
Australian Journal of Intelligent Information Processing Systems 11 (1) (2010) 23e28.
[33] N.K. Kasabov, Evolving fuzzy neural networks e algorithms, applications and biolog-
ical motivation, in: T. Yamakawa, G. Matsumoto (Eds.), Methodologies for the
Conception, Design and Application of Soft Computing, World Scientific, 1998,
pp. 271e274.
[34] N.K. Kasabov, Evolving fuzzy neural networks for on-line supervised/unsupervised,
knowledgeebased learning, IEEE Transactions on Systems, Man, and Cybernetics,
Part B: Cybernetics 31 (6) (2001) 902e918.
[35] N.K. Kasabov, Evolving connectionist systems: methods and applications in bioinfor-
matics, in: Brain Study and Intelligent Machines, Springer-Verlag, London, England,
2002.
[36] N.K. Kasabov, Q. Song, DENFIS: dynamic evolving neural-fuzzy inference system
and its application for time-series prediction, IEEE Transactions on Fuzzy Systems
10 (2) (2002) 144e154.
[37] N.K. Kasabov, Evolving Connectionist Systems: The Knowledge Engineering
Approach, second ed., Springer-Verlag, London, England, 2007.