A Novel Adaptive Algorithm with
Optimum Rates of Convergence

By Dr. Sally Shao

Department of Mathematics
Cleveland State University

Percy P.C. Yip
AI Ware, Inc., Beachwood


We propose a new adaptive algorithm with decreasing step-size for stochastic approximations. The use of adaptive algorithm is widely spread in various applications across the fields such as system identification and adaptive control. We analyze the rate of convergence of the proposed algorithms. An averaging algorithm, on its optimality of the rate of convergence, is using to control the step sizes. Our proofs are based on recent results in stochastic approximations and Guass approximation Theorem.

To Be Presented At The

3rd Forum On Numerics & Modeling for
Partial Differential Equations

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