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During the movements of aircraft, missiles, and ships, the rudder plays an important role in their direction control. In order to test the parameters of the rudders, we have to manually measure each item one by one in traditional production and manufacturing of rudders, which waste a great quantity of manpower and time. In this paper, we present a new application in rudder fault test by using machine learning technology and recommend a new intelligent method for fault location. The main subject revolves around prediction-oriented problems of multi-dimensional performance parameters data mining and the modeling of classification, including the analysis and processing of data features and the solution of fault location based on classification model. In addition, to improve the accuracy of the classification model, we optimized the random forest (RF) algorithm with the shuffled frog leaping algorithm (SFLA), which we call shuffled frog leaping algorithm-based random forest (SFLA-RF). It effectively solves the problem of voting competition among each tree, which makes the decision of the model more efficient and accurate. In a word, by means of automatic test and intelligent analysis, this new method breaks through the technical bottleneck of low efficiency of parameters test and the shortcomings of traditional rudder fault location.

This note studies the controllability and observability for piecewise time-varying impulsive systems. Necessary and sufficient criteria for complete controllability and observability are established. Two numerical examples are given to illustrate the utility and advantages of our criteria.

SummaryThis paper presents a necessary and sufficient condition for discrete‐time singular Markovian jump linear systems with defective statistics of modes transitions to be regular, causal, and stochastically stable. The static output‐feedback stabilization problem for discrete‐time singular Markovian jump linear system with defective statistics of modes transitions is studied. Furthermore, the defective statistics about modes transitions are studied by considering the uncertain transition probabilities and partially unknown transition probabilities situations account in a composite way. The closed‐loop system is represented in an augmented form, in which input and gain‐output matrices are separated. By virtue of the system augmentation, a sufficient condition for the existence of desired controllers is established. An extension to mode‐independent static output‐feed stabilization is provided as well. Copyright © 2013 John Wiley & Sons, Ltd.