Structural Optimization Using the Simultaneous Perturbation Stochastic Approximation Algorithm
نویسندگان
چکیده
منابع مشابه
Convergence of simultaneous perturbation stochastic approximation for nondifferentiable optimization
In this paper, we consider Simultaneous Perturbation Stochastic Approximation (SPSA) for function minimization. The standard assumption for convergence is that the function be three times differentiable, although weaker assumptions have been used for special cases. However, all work that we are aware of at least requires differentiability. In this paper, we relax the differentiability requireme...
متن کاملSimultaneous-Perturbation-Stochastic-Approximation Algorithm for Parachute Parameter Estimation
This paper presents an algorithm to estimate unknown parameters of parachute models from flight-test data. The algorithm is based on the simultaneous-perturbation-stochastic-approximation method to minimize the prediction error (difference between model output and test data). The algorithm is simple to code and requires only the model output. Analytical gradients are not necessary. The algorith...
متن کاملSimultaneous perturbation stochastic approximation of nonsmooth functions
A simultaneous perturbation stochastic approximation (SPSA) method has been developed in this paper, using the operators of perturbation with the Lipschitz density function. This model enables us to use the approximation of the objective function by twice differentiable functions and to present their gradients by volume integrals. The calculus of the stochastic gradient by means of this present...
متن کاملOptimisation of particle filters using simultaneous perturbation stochastic approximation
This paper addresses the optimisation of particle filtering methods aka Sequential Monte Carlo (SMC) methods using stochastic approximation. First, the SMC algorithm is parameterised smoothly by a parameter. Second, optimisation of an average cost function is performed using Simultaneous Perturbation Stochastic Approximation (SPSA). Simulations demonstrate the efficiency of our algorithm.
متن کاملOPTIMISATION OF PARTICLE FILTERS USING SIMULTANEOUS PERTURBATION STOCHASTIC APPROXIMATION tBao
This paper addresses the optimisation of particle filtering methods aka Sequential Monte Carlo (SMC) methods using stochastic approximation. First, the SMC algorithm is parameterised smoothly by a parameter. Second, optimisation of an average cost function is performed using Simultaneous Perturbation Stochastic Approximation (SPSA). Simulations demonstrate the efficiency of our algorithm.
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Algorithms & Computational Technology
سال: 2009
ISSN: 1748-3026,1748-3026
DOI: 10.1260/174830108788251791