Titlebook: Analysis and Design of Markov Jump Systems with Complex Transition Probabilities; Lixian Zhang,Ting Yang,Yanzheng Zhu Book 2016 Springer I

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https://doi.org/10.1007/978-3-662-62570-5own and uncertain transition probabilities (TPs). Therefore, the scenario is more practical and such TPs comprise three sorts: known, uncertain and unknown. Moreover, the system considered in this chapter is specifically meant to be a class of Markov jump neural networks (MJNNs) with uncertainties a

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Datengrundlage und Untersuchungsdesign,probabilities (TPs) in discrete-time domain. The time-varying character of TPs is considered as finite piecewise homogeneous and the variations in the finite set are considered as two types: arbitrary variation and stochastic variation, respectively. The latter means that the variation is subject to

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,Die soziale Dimension der Pädagogik,nties (which cause the variations of the system modes to be subject to a semi-Markov chain). The underlying systems are considered to be approximated by Takagi–Sugeno (T–S) fuzzy models. By means of the semi-Markov kernel, the probability density function of the sojourn time for different modes in d

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Studies in Systems, Decision and Controlhttp://image.papertrans.cn/a/image/156193.jpg