Efficiency comparison and parameter sensitivity of deterministic and stochastic search methods

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Published **1996**
by University of the Federal Armed Forces Munich, Faculty of Aero-Space Engineering, Institute of Mathematics and Computer Sciences in Neubiberg .

Written in English

- Stochastic processes.,
- Search theory.,
- Mathematical optimization.

**Edition Notes**

Statement | K.-J. Böttcher. |

Series | Mitteilung aus dem Forschungsschwerpunkt Simulation und Optimierung Deterministischer und Stochastischer Dynamischer Systeme |

Classifications | |
---|---|

LC Classifications | QA274 .B665 1996 |

The Physical Object | |

Pagination | 120 leaves : |

Number of Pages | 120 |

ID Numbers | |

Open Library | OL760583M |

LC Control Number | 97157376 |

To generate the same predictive power with stochastic simulations, one would need to increase and decrease each of the 18 parameters, for a total of × 18 × 2 = simulations. The logistic regression method is therefore more efficient than one-at-a-time parameter sensitivity analysis by a factor of / = Cited by: Deterministic vs. stochastic models • In deterministic models, the output of the model is fully determined by the parameter values and the initial conditions. • Stochastic models possess some inherent randomness. The same set of parameter values and initial conditions will . Cooling schedule for c parameter used here is a linear decreasing with rate Solving the problem by a hybrid method combining deterministic and stochastic methods Stochastic methods have the advantage of avoiding local minima and the ability of providing solutions when dealing with complex by: 3. This methodology is then used to study sensitivity, robustness, and parameter identifiability in stochastic chemical kinetics models. We show that significant differences exist between stochastic and deterministic models as well as between stochastic models with time-series and time-point measurements.

Estimation of parameter sensitivities for stochastic chemical reaction networks is an important and challenging problem. Sensitivity values are important in the analysis, modeling, and design of chemical networks. They help in understanding the robustness properties of the system and also in identifying the key reactions for a given outcome. In a discrete setting, most of the methods that. We present a parameter sensitivity analysis method that is appropriate for stochastic models, and we demonstrate how this analysis generates experimentally testable predictions about the factors that influence local Ca 2+ release in heart cells. The method involves randomly varying all parameters, running a single simulation with each set of parameters, running simulations with . In the adjustment of inertial position surveys the additional parameters describing the systematic errors of individual traverses can be considered as deterministic or stochastic. The paper deals with various aspects of the deterministic or stochastic approach by way of a standard functional model. If purely deterministic parameters are set up, the solvability of the least squares problem. J. Leng, in Recent Trends in Cold-Formed Steel Construction, Stochastic search algorithms. Stochastic search algorithms are designed for problems with inherent random noise or deterministic problems solved by injected randomness. In structural optimization, these are problems with uncertainties of design variables or those where adding random perturbation to deterministic design.

The SSI and MISA are commonly used for sensitivity analysis of problems with independent model parameters,,.However, problems with correlated model parameters, which arise from inherent parameter dependences or parameter identification procedures, are the standard in chemical processes and other industrial applications,,,.The parameter space is restricted by the . Piecewise continuous reconstruction of real-valued data can be formulated in terms of nonconvex optimization problems. Both stochastic and deterministic algorithms have been devised to solve them. The simplest such reconstruction process is the weak string. Exact solutions can be obtained for it and are used to determine the success or failure of the algorithms under precisely controlled. Relation () helps us in viewing parameter sensitivity as the sum of two parts: sensitivity of the reaction fluxes and the sensitivity of the states x θ (t). In, we provide such a decomposition for parameter sensitivity in the stochastic setting. However, unlike the deterministic scenario, measuring the second contribution is. Several deterministic and stochastic approximation methods are provided: Taylor expansion methods, regression and response surface methods (RSM), .

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