STOCHASTIC APPROXIMATION TECHNIQUES APPLIED TO PARAMETER ESTIMATION IN A BIOLOGICAL MODEL

Authors

  • C. Renotte
  • A. Vande Wouwer

DOI:

https://doi.org/10.47839/ijc.2.2.211

Keywords:

Stochastic approximation, optimization, nonlinear identification, biotechnology

Abstract

Simultaneous perturbation stochastic approximation (SPSA) is a class of optimization algorithms which compute an approximation of the gradient and/or the Hessian of the objective function by varying all the elements of the parameter vector simultaneously and therefore, require only a few objective function evaluations to obtain first or second-order information. Consequently, these algorithms are particularly well suited to problems involving a large number of design parameters. In this study, their potentialities are assessed in the context of nonlinear system identification. To this end, a challenging modeling application is considered, i.e. dynamic modeling of batch animal cell cultures from sets of experimental data. The performance of the optimization algorithms are discussed in terms of efficiency, accuracy and ease of use.

References

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C. Renotte, A. Vande Wouwer, M. Remy. Neural Modeling and Control of a Heat Exchanger based on SPSA Techniques. Proceedings of the Workshop “ACC”, 2000, pp.3299-3303.

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A. Vande Wouwer, C. Renotte, M. Remy. On the Use of Simultaneous Perturbation Stochastic Approximation for Neural Network Training. Proceedings of the Workshop “ACC”, 1999, pp.388-392.

X. Zhu, J. Spall. A Modified Second Order SPSA Optimization Algorithm for Finite Samples, Int. J. of Adaptive Control and Signal Processing 16 (2002). pp. 397-409.

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Published

2014-08-01

How to Cite

Renotte, C., & Vande Wouwer, A. (2014). STOCHASTIC APPROXIMATION TECHNIQUES APPLIED TO PARAMETER ESTIMATION IN A BIOLOGICAL MODEL. International Journal of Computing, 2(2), 87-92. https://doi.org/10.47839/ijc.2.2.211

Issue

2003, Volume 2, Issue 2

Section

Articles

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