Abstract-In Dempster-Shafer evidence theory (DST) based classifier design, Dempster's combination (DC) rule is commonly used as a multi-attribute classifier to combine evidence collected from different attributes. The main aim of this paper is to present a classification method using a novel combination rule i.e., the evidence reasoning (ER) rule. As an improvement of the DC rule, the newly proposed ER rule defines the reliability and weight of evidence. The former indicates the ability of attribute or its evidence to provide correct assessment for classification problem, and the latter reflects the relative important of evidence in comparison with other evidence when they need to be combined. The ER rule-based classification procedure is expatiated from evidence acquisition and estimation of evidence reliability and weight to combination of evidence. It is a purely data-driven approach without making any assumptions about the relationships between attributes and class memberships, and the specific statistic distributions of attribute data. Experiential results on five popular benchmark databases taken from University of California Irvine (UCI) machine learning database show high classification accuracy that is competitive with other classical and mainstream classifiers.

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Summary In this paper, mixed passive / H∞ hybrid controllers are designed for a delayed Markovian jump system with actuator constraints and alarm signal. The actuator constraints include the unknown actuator bias failure and the actuator partial failure, and the controllers are switched by using alarm signals. First, a sufficient condition for mixed passive / H∞ performance of the closed‐loop system is constructed, and robust controllers and fault‐tolerant controllers are designed, respectively. Then, mixed passive / H∞ observers are presented to ensure that the actuator bias failure is well estimated. Next, an alarm system is proposed by designing multiple thresholds to avoid false alarm, and this system is used to invoke suitable controllers and determine whether to remove the estimated value of actuator bias failure. Finally, two numerical examples are given to illustrate the effectiveness of the method.

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