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This paper deals with the issue of observer-basedH∞resilient control for a class of switched linear parameter-varying (LPV) systems by utilising a multiple parameter-dependent Lyapunov functions method. First, attention is focused upon the design of a resilient observer, an observer-based resilient controller and a parameter and estimate state-dependent switching signal, which can stabilise and achieve the disturbance attenuation for the given systems. Then, a solvability condition of theH∞resilient control problem is given in terms of matrix inequality for the switched LPV systems. This condition allows theH∞resilient control problem for each individual subsystem to be unsolvable. The observer, controller, and switching signal are explicitly computed by solving linear matrix inequalities (LMIs). Finally, the effectiveness of the proposed control scheme is illustrated by its application to a turbofan engine, which can hardly be handled by the existing approaches. [ABSTRACT FROM AUTHOR]

This paper investigates theF-policy queue using fuzzy parameters, in which the arrival rate, service rate, and start-up rate are all fuzzy numbers. TheF-policy deals with the control of arrivals in a queueing system, in which the server requires a start-up time before allowing customers to enter. A crispF-policy queueing system generalised to a fuzzy environment would be widely applicable; therefore, we apply the α-cuts approach and Zadeh's extension principle to transform fuzzyF-policy queues into a family of crispF-policy queues. This study presents a mathematical programming approach applicable to the construction of membership functions for the expected number of customers in the system. Furthermore, we propose an efficient solution procedure to compute the membership function of the expected number of customers in the system under different levels of α. Finally, we give an example of the proposed system as applied to a case in the automotive industry to demonstrate its practicality. [ABSTRACT FROM PUBLISHER]

In this paper, the event-triggered exponential filtering problem is addressed for a class of networked linear switched systems via mode-dependent average dwell time (MDADT) technique. An event-triggered communication mechanism is designed to enhance the resource utilisation in transmission and save the communication cost of systems as well. Then, the filtering error system is reconstructed as a switched time-delay system with bounded disturbance by virtue of the input delay system approach. By applying the mode-dependent Lyapunov functional approach, some interesting results with MDADT technique are derived to ensure the exponential stability and a prescribed disturbance rejection level. Further, some sufficient conditions for switched systems with average dwell time (ADT) are also presented as byproduct. An event-triggered exponential filter is presented by solving some convex optimisation problems. The efficiency of the presented results is verified through a numerical example and a PWM-driven boost converter systems. [ABSTRACT FROM AUTHOR]

The article discusses various reports published within the issue, including one on an approach to combine objectivity with modeling process to monitor the frequency behavior of item-sets and another on a model fitness evaluation method to identify positive and negative instances effectively.

This paper investigates the problem of the input-to-state practical stability for switched affine systems with time-varying delay and essentially bounded exogenous disturbance. The presence of affine terms makes switched systems have multiple switched equilibria and brings further difficulties in the stability analysis, control synthesis and avoiding Zeno behaviours compared with the non-affine switched systems. To overcome these difficulties, first, a convex combination approach is employed to acquire a set of attainable equilibria limited by the system matrix, the delay term matrix, and affine terms. Second, a mode-dependent event-triggered mechanism with sampled-data information is established, which not only ignores Zeno behaviour but also effectively reduces network resource waste. An input-to-state practical stability criterion is proposed for the closed-loop system by converting the event-triggered control system into a time-delay system and introducing a less conservative time-dependent Lyapunov–Krasovskii functional. Moreover, a sampled-data switching law and a set of executable controllers are also co-designed. Finally, an application example of the flyback DC–DC converter demonstrates the efficiency of the proposed result. [ABSTRACT FROM AUTHOR]

Document classification is a supervised machine learning process, where predefined category labels are assigned to documents based on the hypothesis derived from training set of labelled documents. Documents cannot be directly interpreted by a computer system unless they have been modelled as a collection of computable features. Rogati and Yang [M. Rogati and Y. Yang, “Resource selection for domain-specific cross-lingual IR”, in SIGIR 2004: Proceedings of the 27th annual international conference on Research and Development in Information Retrieval, ACM Press, Sheffied: United Kingdom, pp. 154–161.] pointed out that the effectiveness of document classification system may vary in different domains. This implies that the quality of document model contributes to the effectiveness of document classification. Conventionally, model evaluation is accomplished by comparing the effectiveness scores of classifiers on model candidates. However, this kind of evaluation methods may encounter either under-fitting or over-fitting problems, because the effectiveness scores are restricted by the learning capacities of classifiers. We propose a model fitness evaluation method to determine whether a model is sufficient to distinguish positive and negative instances while still competent to provide satisfactory effectiveness with a small feature subset. Our experiments demonstrated how the fitness of models are assessed. The results of our work contribute to the researches of feature selection, dimensionality reduction and document classification. [ABSTRACT FROM AUTHOR]

Fuzzy data has grown to be an important factor in data mining. Whenever uncertainty exists, simulation can be used as a model. Simulation is very flexible, although it can involve significant levels of computation. This article discusses fuzzy decision-making using the grey related analysis method. Fuzzy models are expected to better reflect decision-making uncertainty, at some cost in accuracy relative to crisp models. Monte Carlo simulation is used to incorporate experimental levels of uncertainty into the data and to measure the impact of fuzzy decision tree models using categorical data. Results are compared with decision tree models based on crisp continuous data. [ABSTRACT FROM AUTHOR]

This paper proposes the almost output regulation model reference adaptive control problem for switched systems with parametric uncertainties and multiple types of disturbances. By the almost output regulation model reference adaptive control strategy, the parametric uncertainties are suppressed and the multiple types of disturbances are rejected, simultaneously. In order to improve the transient performance of switched systems, the combined model reference adaptive control strategy is applied. A key point is to set up the almost output regulation model reference adaptive control strategy to achieve the tracking and the disturbance rejection performance. First, we design a switched identification model to estimate the plant uncertain parameters, by which more accurate plant parameters will be obtained. Secondly, based on the switched identification model, controllers with combined adaptive laws and a state-dependent switching law are designed to solve the almost output regulation model reference adaptive control problem for switched systems. Finally, a sufficient condition is given, which guarantees that the problem of the almost output regulation model reference adaptive control for switched systems is solvable in spite of the problem of almost output regulation model reference adaptive control for each subsystem is not solvable. [ABSTRACT FROM AUTHOR]

This paper considers the output-feedback fault-tolerant tracking control problem for a class of uncertain nonlinear switched systems with nonlinear faults and strict-feedback form, where the faults which are nonaffine occur on the actuator. As a kind of specialised function approximating tool, fuzzy logic systems (FLSs), are employed to approximate the unknown smooth nonlinear functions. A switched fuzzy observer is designed to address the problem of unmeasurable states, filtered signals are used to address algebraic loop problem and the average dwell time (ADT) method is further utilised to prove the stability of the resulting closed-loop systems under a type of slowly switching signals. Based on the backstepping recursive design technique and Lyapunov function method, an adaptive fuzzy output-feedback control scheme is developed. The developed control method can ensure all the signals are semi-globally uniformly ultimately bounded (SGUUB) and the system output tracks the reference signal tightly even if unknown fault occurs. A simulation carried on an example demonstrates the validity of the obtained control scheme. [ABSTRACT FROM AUTHOR]