Abstract

Modeling of biotechnological systems is an important research area. The most challenging approach is to build non-linear state space models for these systems. In this work the parameters of a bacterial growth bioprocess are estimated using prediction error method. Prediction error methods are widely used in parameter estimation both for linear and nonlinear models and consist in minimization of the distance between measured and modeled data in a suitable norm. Because these problems are solved using numerical algorithms that are time consuming, in this paper a parallel particle swarm optimization technique is used in order to numerically solve the minimization problem. The algorithm is implemented on a multicore processor and the performances of this approach are presented by numerical simulations.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.