A dynamic all parameters adaptive BP neural networks model and its application on oil reservoir prediction

Abstract In this paper, a dynamic all parameters adaptive BP neural networks model is proposed by fusing genetic algorithms (GAs), simulated annealing (SA) and error back propagation neural network (BPNN) to offset the demerits of one paradigm by the merits of another. Adopting multi-encoding, the model can optimize the input nodes, hidden nodes, transfer function, weights and bias of BP networks dynamically and adaptively. Under accurate premise, the simple architecture (less input and hidden nodes) of network model is constructed in order to improve networks’ adaptation and generalization ability, and to greatly reduce the subjective choice of structural parameters. The results of application on oil reservoir prediction show that the proposed model with comparatively simple structure can meet the precision request and enhance the generalization ability.

[1]  Holger R. Maier,et al.  The effect of internal parameters and geometry on the performance of back-propagation neural networks: an empirical study , 1998 .

[2]  Mikko Kolehmainen,et al.  Evolving the neural network model for forecasting air pollution time series , 2004, Eng. Appl. Artif. Intell..

[3]  O. Tapias,et al.  Reservoir Engineer and Artificial Intelligence Techniques for Data Analysis , 2001 .

[4]  T. R. Bement,et al.  Taguchi techniques for quality engineering , 1995 .

[5]  Kazuyuki Murase,et al.  A new algorithm to design compact two-hidden-layer artificial neural networks , 2001, Neural Networks.

[6]  B. S. Lim,et al.  Optimal design of neural networks using the Taguchi method , 1995, Neurocomputing.

[7]  Khashayar Khorasani,et al.  A new strategy for adaptively constructing multilayer feedforward neural networks , 2003, Neurocomputing.

[8]  G. Casalino,et al.  An ANN and Taguchi algorithms integrated approach to the optimization of CO2 laser welding , 2006, Adv. Eng. Softw..

[9]  Lalit M. Patnaik,et al.  Adaptive probabilities of crossover and mutation in genetic algorithms , 1994, IEEE Trans. Syst. Man Cybern..

[10]  De-Shuang Huang,et al.  Prediction of inter-residue contacts map based on genetic algorithm optimized radial basis function neural network and binary input encoding scheme , 2004, J. Comput. Aided Mol. Des..

[11]  Holger R. Maier,et al.  Understanding the behaviour and optimising the performance of back-propagation neural networks: an empirical study , 1998 .

[12]  Quanmin Zhu,et al.  A back propagation algorithm to estimate the parameters of non-linear dynamic rational models , 2003 .

[13]  Wang Li-jun.  Study of improving algorithms based on the BP neural network , 2005 .

[14]  George-Christopher Vosniakos,et al.  Prediction of surface roughness in CNC face milling using neural networks and Taguchi's design of experiments , 2002 .

[15]  Juan Julián Merelo Guervós,et al.  G-Prop: Global optimization of multilayer perceptrons using GAs , 2000, Neurocomputing.

[16]  Xudong Jiang,et al.  Constructing and training feed-forward neural networks for pattern classification , 2003, Pattern Recognit..

[17]  Wu Qi-zong.  Forecast on Temporal Sequence of Railway Freight Transport Volume based on BP-SA Mixing and Optimizing Solution , 2003 .

[18]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[19]  Yong Liu,et al.  A GA-based NN approach for makespan estimation , 2007, Appl. Math. Comput..

[20]  Christian Lebiere,et al.  The Cascade-Correlation Learning Architecture , 1989, NIPS.

[21]  Ihsan Sabuncuoglu,et al.  A neural network model for scheduling problems , 1996 .

[22]  Jürgen Bode,et al.  Neural networks for cost estimation: Simulations and pilot application , 2000 .

[23]  Timo Poranen,et al.  A simulated annealing algorithm for determining the thickness of a graph , 2005, Inf. Sci..

[24]  Li Zhang,et al.  An evaluation of back-propagation neural networks for the optimal design of structural systems: Part I. Training procedures , 2002 .

[25]  Y. L. Loukas,et al.  Artificial neural networks in liquid chromatography: efficient and improved quantitative structure-retention relationship models. , 2000, Journal of chromatography. A.

[26]  Katsumi Morikawa,et al.  Neural network approach for minimizing the makespan of the general job-shop , 1994 .

[27]  J. Keith Ord,et al.  Automatic neural network modeling for univariate time series , 2000 .

[28]  W. W. Weiss,et al.  Using Artificial Intelligence to Corellate Multiple Seismic Attributes to Reservoir Properties , 1999 .

[29]  Jasmina Arifovic,et al.  Using genetic algorithms to select architecture of a feedforward artificial neural network , 2001 .