Bibliografía

1

C. Aldrich and J. S. J. van Deventer.
Comparison of different artificial neural nets for the detection and location of gross errors in process systems.
Industrial & Engineering Chemistry Research, 34(1):216-224, 1995.

2

C. M. Bishop.
Neural Networks for Pattern Recognition.
Oxford University Press, USA, 1995.

3

C. M. Bishop.
Pattern recognition and machine learning.
Springer, 2006.
(Online service).

4

G. J. Bowden, G. C. Dandy, and H. R. Maier.
Data transformation for neural network models in water resources applications.
Journal of Hydroinformatics, 5(4):245-258, 2003.

5

M. Brown and C. Harris.
Neurofuzzy adaptive modelling and control.
Prentice Hall, 1995.

6

Y. Chauvin and D. E. Rumelhart.
Backpropagation: Theory, Architectures, and Applications.
Lawrence Erlbaum Associates, 1995.

7

J. Faraway and C. Chatfield.
Time series forecasting with neural networks: a comparative study using the air line data.
Journal of the Royal Statistical Society: Series C: Applied Statistics, 47(2):231-250, 1998.

8

J. A. Freeman and D. M. Skapura.
Neural Networks: Algorithms, Applications, and Programming Techniques.
Addison-Wesley, 1991.

9

R. González-García, R. Rico Martínez, and I. G. Kevrekidis.
Identification of distributed parameter systems: A neural net based approach.
Computers and Chemical Engineering, 22:965-968, 1998.

10

S. Haykin.
Neural Networks: A Comprehensive Foundation.
Prentice Hall, NJ, USA, 2008.

11

J. R. Hilera and V. J. Martinez.
Redes neuronales artificiales. Fundamentos, modelos y aplicaciones.
Addison-Wesley Iberoamericana S.A, Madrid, 1995.

12

J. C. Hoskins and D. M. Himmelblau.
Process control via artificial neural networks and reinforcement learning.
Computers & chemical engineering, 16(4):241-251, 1992.

13

T. Kohonen.
Self-organization and associative memory.
Springer Verlag, New York, 1989.

14

C. T. Lin and C. S. G. Lee.
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems.
Prentice-Hall, Inc. Upper Saddle River, NJ, USA, 1996.

15

C. G. Looney.
Pattern recognition using neural networks: theory and algorithms for engineers and scientists.
Oxford University Press, Inc. New York, NY, USA, 1997.

16

W. S. McCulloch and W. Pitts.
A logical calculus of the ideas immanent in nervous activity.
Bulletin of Mathematical Biology, 5(4):115-133, 1943.

17

K. Meert and M. Rijckaert.
Intelligent modelling in the chemical process industry with neural networks: a case study.
Computers and Chemical Engineering, 22:587-593, 1998.

18

K. S. Narendra, M. J. Feiler, and Z. Tian.
Control of complex systems using neural networks.
Modeling and Control of Complex Systems, 2008.

19

C. A. O. Nascimento, R. Giudici, and R. Guardani.
Neural network based approach for optimization of industrial chemical processes.
Computers and Chemical Engineering, 24(9-10):2303-2314, 2000.

20

Alejandro Pazos.
Redes de neuronas artificiales y algoritmos genéticos.
Servicio de Publicaciones Universidade da Coruña, 1996.

21

B. A. Pearlmutter.
Dynamic recurrent neural networks.
Technical report, Technical Report CMU-CS. School of Computer Science, Carnegie Mellon University, 1990., 1990.

22

I. Rivals and L. Personnaz.
Nonlinear internal model control using neural networks: application to processes with delay and design issues.
IEEE Transactions on Neural Networks, 11(1):80-90, 2000.

23

J. J. Shi.
Reducing prediction error by transforming input data for neural networks.
Journal of Computing in Civil Engineering, 14:109, 2000.

24

P. D. Wasserman.
Neural computing: theory and practice.
Van Nostrand Reinhold Co. New York, NY, USA, 1989.

25

N. Wiener.
God and Golem: a Comment on Certain Points where Cybernetics Impinges on Religion.
The MIT Press, 1964.