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This paper is concerned with the problem of H ∞ filtering for discrete-time Markov jump linear system with parametric uncertainties and quantized measurements, when the jumping mode information is not accessible. By converting the quantized errors into a sector-bounded nonlinearity, the parametric uncertainties and measurements quantization are dealt with in a unified framework. The mode-independent H ∞ filter is designed, and sufficient conditions are established via Lyapunov function approach, such that for all possible uncertain parameters and quantization errors, the resulting filtering error system is robustly stochastically stable and achieves a guaranteed H ∞ filtering error performance index. A numerical example is provided to demonstrate the feasibility and effectiveness of the proposed approach. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This study is concerned with the problem of state estimation for networked systems with a Markov plant considering the measurement uncertainties. The measurements suffer from both the randomly occurring missing phenomenon and the quantisation effects. Taking into account the statistical knowledge of the quantised measurements, an approximate minimum mean square error estimate algorithms is derived based on Gaussian assumption, which is referred to as interacting multiple model Monte Carlo (IMMMC) algorithm. A quantised measurement expectation calculated by Monte Carlo sampling method is embedded into the Kalman filter under the IMM framework. A simulation example is provided demonstrating that IMMMC is computationally appealing and presents better estimate performance than the previous algorithms. Moreover, IMMMC has better mode following ability and can clearly distinguish the occurrence of measurements missing. [ABSTRACT FROM AUTHOR]

The problem of robust H∞ filtering is investigated for discrete-time Markov jump linear system (DMJLS) with uncertain parameters and missing measurements. The missing measurements process is modelled as a Bernoulli distributed sequence. A robust H∞ filter is designed and sufficient conditions are established in terms of linear matrix inequalities via a mode-dependent Lyapunov function approach, such that, for all admissible uncertain parameters and missing measurements, the resulting filtering error system is robustly stochastically stable and a guaranteed H∞ performance constraint is achieved. Furthermore, the optimal H∞ performance index is subsequently obtained by solving a convex optimisation problem and the missing measurements effects on the H∞ performance are evaluated. A numerical example is given to illustrate the feasibility and effectiveness of the proposed filter. [ABSTRACT FROM AUTHOR]

In the chemical industry, fault diagnosis is a challenging task due to the complexity and the high demand of product efficiency and consistency in the chemical plant. In this paper, an improved Case-based reasoning method is proposed to predict the status of Tennessee Eastman process (TE process). Firstly, a case reduction method based on selective rules is proposed to maintain the scale of the case base for decreasing the time complexity of the diagnosis process. Afterwards, a case reuse strategy based on trustworthy radius is introduced with the group decision making, this reuse strategy is utilized to evaluate the suggested diagnosis result as trustworthy or untrustworthy by calculating out the trustworthy radius of each category. Finally, the cases that are evaluated as untrustworthy would be adjusted with the group decision making strategy to get the final diagnosis result to accomplish the whole diagnosis process of CBR. Compared with other methods, the diagnosis results obtained from the proposed approach showed higher accuracy and reliability, which demonstrated the improvement of the diagnosis performance of TE process as well as the effectiveness and superiority of the proposed method on fault diagnosis. [ABSTRACT FROM AUTHOR]