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Artificial Neural Networks

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Computational Intelligence

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References

  • Albus J S, (1975a), “A new approach to manipulator control: cerebellar model articulation control (CMAC)”, Trans. ASME, J. of Dynamics Syst., Meas. and Contr., 97, 220–227.

    MATH  Google Scholar 

  • Albus J S, (1975b), “Data storage in the cerebellar model articulation controller (CMAC)”, Trans. ASME, J. of Dynamics Syst., Meas. and Contr., 97, 228–233.

    MATH  Google Scholar 

  • Albus J S, (1979a), “A model of the brain for robot control”, Byte, 54–95.

    Google Scholar 

  • Albus J S, (1979b), “Mechanisms of planning and problem solving in the brain”, Math. Biosci., 45, 247–293.

    Article  Google Scholar 

  • An P E, Brown M, Harris C J, Lawrence A J and Moore C J, (1994), “Associative memory neural networks: adaptive modelling theory, software implementations and graphical user”, Engng. Appli. Artif. Intell., 7 (1), 1–21.

    Article  Google Scholar 

  • Bohte S M, La Poutre H and Kok J N, (2002a), “Unsupervised clustering with spiking neurons by sparse temporal coding and multilayer RBF networks”, IEEE Trans. on Neural Networks, 13 (2), 415–425.

    Article  Google Scholar 

  • Bohte S M, La Poutre H and Kok J N, (2002b), “Error-back propagation in temporally encoded networks of spiking neurons”, Neuro Computing, 17–37.

    Google Scholar 

  • Broomhead D S and Lowe D, (1988), “Multivariable functional interpolation and adaptive networks”, Complex Systems, 2, 321–355.

    MATH  MathSciNet  Google Scholar 

  • Carpenter G A and Grossberg S, (1987), “ART2: Self-organisation of stable category recognition codes for analog input patterns”, Appl. Optics,26 (23), 4919–4930.

    Article  Google Scholar 

  • Carpenter G A and Grossberg S, (1988), “The ART of adaptive pattern recognition by a self-organising neural network”, Computer, 77–88.

    Google Scholar 

  • Cichocki A and Unbahauen R, (1993), Neural Networks for Optimisation and Signal Processing, Chichester: Wiley.

    Google Scholar 

  • Elman J L, (1990), “Finding structure in time”, Cognitive Science, 14, 179–211.

    Article  Google Scholar 

  • Gerstner W and Kistler W M, (2002), Spiking Neuron Models: Single Neurons, Populations and Plasticity, Cambridge University Press, UK.

    Google Scholar 

  • Goldberg D, (1989), Genetic Algorithms in Search, Optimisation and Machine Learning, Reading, MA: Addison-Wesley.

    Google Scholar 

  • Hassoun M H, (1995), Fundamentals of Artificial Neural Networks, MIT Press, Cambridge, MA.

    MATH  Google Scholar 

  • Haykin S, (1999), Neural Networks: A Comprehensive Foundation, 2nd Edition, Upper Saddle River, NJ: Prentice Hall.

    MATH  Google Scholar 

  • Hecht-Nielsen R, (1990), Neurocomputing, Reading, MA: Addison-Wesley.

    Google Scholar 

  • Holland J H, (1975), Adaptation in Natural and Artificial Systems, Ann Arbor, MI: University of Michigan Press.

    Google Scholar 

  • Hopfield J J, (1982), “Neural networks and physical systems with emergent collective computational abilities”, Proc. National Academy of Sciences, 79, 2554–2558.

    Article  MathSciNet  Google Scholar 

  • Iannella N and Back A D, (2001), Spiking neural network architecture for nonlinear function approximation, Neural Networks, Special Issue, 14(6), 922–931.

    Google Scholar 

  • Jordan M I, (1986), “Attractor dynamics and parallelism in a connectionist sequential machines”, Proc. 8th Annual Conf. of the Cognitive Science Society, 531–546.

    Google Scholar 

  • Karaboga D, (1994), Design of Fuzzy Logic Controllers Using Genetic Algorithms, PhD thesis, University of Wales, Cardiff, UK.

    Google Scholar 

  • Kohonen T, (1989), Self-Organising and Associative Memory (3rd ed.), Berlin: Springer-Verlag.

    Google Scholar 

  • Lannella N and Back A D, (2001), Spiking neural network architecture for nonlinear function approximation, Neural Networks, Special Issue, 14(16), 922-931

    Google Scholar 

  • Maass W, (1997), “Networks of spiking neurons: The third generation of neural network models”, Neural Networks, 10, 1659–1671.

    Article  Google Scholar 

  • Maass W and Bishop C M, (1998), Pulsed Neural Networks, Cambridge: MIT Press.

    MATH  Google Scholar 

  • Moody J and Darken C J, (1989), “Fast learning in networks of locally-tuned processing units”, Neural Computation, 1 (2), 281–294.

    Google Scholar 

  • Natschläger T and Ruf B, (1998), “Spatial and temporal pattern analysis via spiking neurons”, Network: Computation in Neural systems, 9 (3), 319–332.

    Article  MATH  Google Scholar 

  • Pham D T and Chan A J, (1998), “Control chart pattern recognition using a new type of self-organising neural network”, Proc. of the Institution of Mechanical Engineers, 212 (Part I), 115–127.

    Google Scholar 

  • Pham D T and Chan A J, (2001), “Unsupervised adaptive resonance theory neural networks for control chart pattern recognition”, Proc. of the Institution of Mechanical Engineers, 215 (Part B), 59–67.

    Google Scholar 

  • Pham D T and Karaboga D, (1993), “Dynamic system identification using recurrent neural networks and genetic algorithms”, Proc. 9th Int. Conf. on Mathematical and Computer Modelling, San Francisco.

    Google Scholar 

  • Pham D T and Liu X, (1992), “Dynamic system modelling using partially recurrent neural networks”, Journal of Systems Engineering, 2 (2), 90–97.

    Google Scholar 

  • Pham D T and Liu X, (1994), “Modelling and prediction using GMDH networks of Adalines with nonlinear preprocessors”, Int. J. Systems Science, 25 (11), 1743–1759.

    MATH  Google Scholar 

  • Pham D T and Oh S J, (1992), “A recurrent backpropagation neural network for dynamic system identification”, Journal of Systems Engineering, 2 (4), 213–223.

    Google Scholar 

  • Pham D T and Oztemel E, (1994), “Control chart pattern recognition using learning vector quatization networks”, Int. J. Production Research, 32 (3), 721–729.

    MATH  Google Scholar 

  • Rumelhart D and McClelland J, (1986), Parallel distributed processing: exploitations in the micro-structure of cognition, volumes 1 and 2, Cambridge: MIT Press.

    Google Scholar 

  • Widrow B and Hoff M E, (1960), “Adaptive switching circuits”, Proc. 1960 IRE WESCON Convention Record, Part 4, IRE, New York, 96–104.

    Google Scholar 

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Pham, D.T., Packianather, M.S., Afify, A.A. (2007). Artificial Neural Networks. In: Andina, D., Pham, D.T. (eds) Computational Intelligence. Springer, Boston, MA. https://doi.org/10.1007/0-387-37452-3_3

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