Redes Neuronales para identificacin y prediccin de series de tiempo

La prediccion de series de tiempo es un area que ha llamado mucho la atencion debido a la gran cantidad de aplicaciones que tiene en areas como control, economia y medicina, entre otras. En este trabajo se presentan algunos de los algoritmos de redes neuronales artificiales que han mostrado mejores resultados en este campo. Se presenta la aplicacion en la prediccion de la serie de manchas solares como los datos estandar para que pueda ser comparada con otros algoritmos reportados

[1]  Sheng Chen,et al.  Recursive prediction error parameter estimator for non-linear models , 1989 .

[2]  Vincenzo Piuri,et al.  Experimental neural networks for prediction and identification , 1996 .

[3]  Gene F. Franklin,et al.  Digital control of dynamic systems , 1980 .

[4]  Joydeep Ghosh,et al.  Approximation of multivariate functions using ridge polynomial networks , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

[5]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[6]  Mark A. Franklin,et al.  A Learning Identification Algorithm and Its Application to an Environmental System , 1975, IEEE Transactions on Systems, Man, and Cybernetics.

[7]  Petre Stoica,et al.  Decentralized Control , 2018, The Control Systems Handbook.

[8]  Robert J. Marks,et al.  Electric load forecasting using an artificial neural network , 1991 .

[9]  Duc Truong Pham,et al.  Neural Networks for Identification, Prediction and Control , 1995 .

[10]  Ronald J. MacGregor,et al.  Neural and brain modeling , 1987 .

[11]  E. Gomez-Ramirez,et al.  Adaptive architecture of polynomial artificial neural network to forecast nonlinear time series , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[12]  L. Altenberg.  The evolution of evolvability in genetic programming , 1994 .

[13]  I. J. Leontaritis,et al.  Input-output parametric models for non-linear systems Part II: stochastic non-linear systems , 1985 .

[14]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[15]  Peter Nordin,et al.  Genetic programming - An Introduction: On the Automatic Evolution of Computer Programs and Its Applications , 1998 .

[16]  David Andre,et al.  Automatically defined features: the simultaneous evolution of 2-dimensional feature detectors and an algorithm for using them , 1994 .

[17]  J. K. Kinnear,et al.  Advances in Genetic Programming , 1994 .

[18]  Sheng Chen,et al.  Representations of non-linear systems: the NARMAX model , 1989 .

[19]  John Y. Cheung,et al.  Polynomial and standard higher order neural network , 1993, IEEE International Conference on Neural Networks.

[20]  K S Narendra,et al.  IDENTIFICATION AND CONTROL OF DYNAMIC SYSTEMS USING NEURAL NETWORKS , 1990 .

[21]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[22]  Richard Dawkins,et al.  The Evolution of Evolvability , 1987, ALIFE.

[23]  Lee Altenberg,et al.  Genome Growth and the Evolution of the Genotype-Phenotype Map , 1995, Evolution and Biocomputation.

[24]  A. G. Ivakhnenko,et al.  Polynomial Theory of Complex Systems , 1971, IEEE Trans. Syst. Man Cybern..