Titlebook: Stochastic Approximation and Recursive Algorithms and Applications; Harold J. Kushner,G. George Yin Book 19971st edition Springer-Verlag N

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书目名称Stochastic Approximation and Recursive Algorithms and Applications被引频次学科排名




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DRILL

Esalate

产生

杂役

Applications to Learning, State Dependent Noise, and Queueing,y are described in somewhat more detail than the examples of Chapter 1 are, and the illustration(s) given for each class are typical of those in a rapidly increasing literature. Section 1 deals with a problem in learning theory: the learning of an optimal hunting strategy by an animal, based on the

合同

Applications in Signal Processing and Adaptive Control,d there is a large amount of literature concerning them. Only a few references will be listed. Despite the identity in form, whether the algorithms are actually called . depends on the traditions of the field, but the most effective techniques of proofs are often based on those of stochastic approxi

Mechanics

Mathematical Background,ne of the basic processes in stochastic analysis. It appears frequently as a “noise” term in the decomposition of the stochastic approximation algorithm, which will be used to facilitate the analysis. This was already apparent in many examples in Chapters 1 and 2, where the noise had the martingale

轻推

Convergence with Probability One: Martingale Difference Noise,hat is, where there is a function ..(·) of θ such that . [..., . < ., θ.] = ..(θ.) [17, 40, 45, 47, 56, 79, 86, 132, 154, 159, 169, 181]. Then we can write .. = ..(θ.) + δ.. where δ.. is a martingale difference. This “martingale difference noise” model is still of considerable importance. It arises,

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