Estimation of Probabilities of Transitions of a Markov Binary Input Signal of a Nonlinear System

IF 0.5 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
V. A. Boldinov, V. A. Bukhalev, A. A. Skrynnikov, I. F. Khismatov
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引用次数: 0

Abstract

The problem of estimating unknown probabilities of transitions of a random Markov binary input signal of a nonlinear one-dimensional discrete system based on estimating the expectation and variance of the output signal is considered. The defined expressions are built on the basis of considering equally probable transitions and the steady-state mode of the algorithm for assessing the state of the system, obtained by approximating the probability density of its output signal by the Pearson type I distribution. An example of a comparison of the theoretical calculations with the results of imitation mathematical modeling is given.

Abstract Image

非线性系统马尔可夫二进制输入信号转换概率的估算
摘要 本研究考虑了在估计输出信号的期望值和方差的基础上估计非线性一维离散系统的随机马尔可夫二进制输入信号的未知转换概率问题。所定义的表达式建立在考虑等概率转换和评估系统状态算法的稳态模式的基础上,该算法通过皮尔逊 I 型分布近似其输出信号的概率密度获得。文中举例说明了理论计算与模仿数学建模结果的比较。
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来源期刊
Journal of Computer and Systems Sciences International
Journal of Computer and Systems Sciences International 工程技术-计算机:控制论
CiteScore
1.50
自引率
33.30%
发文量
68
审稿时长
6-12 weeks
期刊介绍: Journal of Computer and System Sciences International is a journal published in collaboration with the Russian Academy of Sciences. It covers all areas of control theory and systems. The journal features papers on the theory and methods of control, as well as papers devoted to the study, design, modeling, development, and application of new control systems. The journal publishes papers that reflect contemporary research and development in the field of control. Particular attention is given to applications of computer methods and technologies to control theory and control engineering. The journal publishes proceedings of international scientific conferences in the form of collections of regular journal articles and reviews by top experts on topical problems of modern studies in control theory.
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