Stochastic Approximation for Expensive One-Bit Feedback Systems

• 来源期刊: 清华大学学报自然科学版（英文版）
• 年，卷（期）: 2017,（3）
• 页码范围: 317-327
• 页数: 11页
• 语种: 英文

摘要

One-bit feedback systems generate binary data as their output and the system performance is usually measured by the success rate with a fixed parameter combination.Traditional methods need many executions for parameter optimization.Hence,it is impractical to utilize these methods in Expensive One-Bit Feedback Systems (EOBFSs),where a single system execution is costly in terms of time or money.In this paper,we propose a novel algorithm,named Iterative Regression and Optimization (IRO),for parameter optimization and its corresponding scheme based on the Maximum Likelihood Estimation (MLE) method and Particle Swarm Optimization (PSO)method,named MLEPSO-IRO,for parameter optimization in EOBFSs.The IRO algorithm is an iterative algorithm,with each iteration comprising two parts:regression and optimization.Considering the structure of IRO and the Bernoulli distribution property of the output of EOBFSs,MLE and a modified PSO are selected to implement the regression and optimization sections,respectively,in MLEPSO-IRO.We also provide a theoretical analysis for the convergence of MLEPSO-IRO and provide numerical experiments on hypothesized EOBFSs and one real EOBFS in comparison to traditional methods.The results indicate that MLEPSO-IRO can provide a much better result with only a small amount of system executions.