Many adaptive algorithms perform stochastic gradient descent on performance surfaces that are not guaranteed to be unimodal. In some examples, it is possible to show that not only is there more than one stationary point on this performance surface, but also that there is at least one incorrect local
Convergence analysis of gradient descent stochastic algorithms
โ Scribed by A. Shapiro; Y. Wardi
- Publisher
- Springer
- Year
- 1996
- Tongue
- English
- Weight
- 765 KB
- Volume
- 91
- Category
- Article
- ISSN
- 0022-3239
No coin nor oath required. For personal study only.
๐ SIMILAR VOLUMES
In this paper, we consider the rate of convergence of the parameter estimation error and the cost function for the stochastic gradient-type algorithm. The problem is solved in the case of the minimum-variance stochastic adaptive control. It is proven that the cost function has the rate of convergenc
This paper studies the convergence of the stochastic gradient identification algorithm of multi-input multi-output ARX-like systems (i.e., multivariable ARX-like systems) by using the stochastic martingale theory. This ARX-like model contains a characteristic polynomial and differs from the conventi