دانلود مقاله ISI انگلیسی شماره 79582
ترجمه فارسی عنوان مقاله

تقریب تابع ارزش تطبیقی برای برنامه نویسی پویای تصادفی پیوسته حالت

عنوان انگلیسی
Adaptive value function approximation for continuous-state stochastic dynamic programming
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
79582 2013 9 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Computers & Operations Research, Volume 40, Issue 4, April 2013, Pages 1076–1084

ترجمه کلمات کلیدی
برنامه نویسی پویا تقریبی؛ طراحی پی در پی از آزمایش ها؛ مدل سازی آماری؛ شبکه عصبی؛ تعداد روش های نظری؛ پیش بینی موجودی
کلمات کلیدی انگلیسی
Approximate dynamic programming; Sequential design of experiments; Statistical modeling; Neural network; Number theoretic methods; Inventory forecasting
پیش نمایش مقاله
پیش نمایش مقاله  تقریب تابع ارزش تطبیقی برای برنامه نویسی پویای تصادفی پیوسته حالت

چکیده انگلیسی

Approximate dynamic programming (ADP) commonly employs value function approximation to numerically solve complex dynamic programming problems. A statistical perspective of value function approximation employs a design and analysis of computer experiments (DACE) approach, where the “computer experiment” yields points on the value function curve. The DACE approach has been used to numerically solve high-dimensional, continuous-state stochastic dynamic programming, and performs two tasks primarily: (1) design of experiments and (2) statistical modeling. The use of design of experiments enables more efficient discretization. However, identifying the appropriate sample size is not straightforward. Furthermore, identifying the appropriate model structure is a well-known problem in the field of statistics. In this paper, we present a sequential method that can adaptively determine both sample size and model structure. Number-theoretic methods (NTM) are used to sequentially grow the experimental design because of their ability to fill the design space. Feed-forward neural networks (NNs) are used for statistical modeling because of their adjustability in structure-complexity . This adaptive value function approximation (AVFA) method must be automated to enable efficient implementation within ADP. An AVFA algorithm is introduced, that increments the size of the state space training data in each sequential step, and for each sample size a successive model search process is performed to find an optimal NN model. The new algorithm is tested on a nine-dimensional inventory forecasting problem.