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This paper proposes an interval observer design for a large class of uncertain LTI systems. The proposed methodology estimates simultaneously the upper and lower bounds of unmeasurable states and unknown inputs. The initial model is transformed into a singular model representation without unknown input reducing the conservatism related to the propagation of uncertainties in set membership approach...
The focal distance is used to describe the degree of dissimilarity between focal elements in a basic belief assignment (BBA) by jointly using the focal elements' composition and their corresponding mass assignments. It has already been used in applications such as the design of the uncertainty degree of a BBA, and has been used as a criterion to remove focal elements in BBA approximations. The related...
Bayesian Networks provide an established approach to classification of objects in various types of activities. These range from technical via medical to economical applications. Heterogeneous sources provide observed declarations, which are evaluated by calculating the probabilities of resulting classes based on an underlying Bayesian Network model. Applying this classification process at different...
The use of expert knowledge is always more or less afflicted with uncertainties for many reasons: Expert knowledge may be imprecise, imperfect, or erroneous, for instance. If we ask several experts to label data (e.g., to assign class labels to given data objects, i.e. samples), we often state that these experts make different, sometimes conflicting statements. The problem of labeling data for classification...
Interval programming problems are ubiquitous in real-world situations. There exist a variety of theories and methods for handling them; the existing methods, however, have adopted various dominance criteria to distinguish solutions, and these criteria are always subjective. Different dominance criteria will produce different optimal solution(s), and subjective criteria make users, especially for those...
This paper proposes a method based on unknown input method to synthesize an observer for uncertain Takagi-Sugeno fuzzy system with multiple output matrices where the upper bound constraint of uncertainties are not known either. Firstly, the mathematic transformation is employed to transform the conventional T-S fuzzy system into new form. Secondly, the unknown input method is applied to eliminate...
n-dimensional fuzzy sets are an extension of fuzzy sets where the membership values are n-truples of real numbers in the unit interval [0, 1] ordered in increasing order, called n-dimensional intervals. The set of n-dimensional intervals is denoted by Ln([0, 1]). This paper aims to investigate the class of functions on Ln([0, 1]) which are continuous and strictly decreasing, called n-dimensional strict...
We propose a model to study short-term interbank lending from a network formation perspective. Banks, being provided with public and private signals about the solvency of other banks, decide on interbank lending by also considering the decision of other banks to lend. We observe that the dominant equilibrium networks are those where banks follow each others' decisions, making the equilibria very vulnerable...
Generalised Modus Ponens (GMP) allows to perform logical inference in the case where an observation partially matches the premise of an implication, enriching the rule exploitation as compared to binary classical logic. This paper proposes to further enhance the rule exploitation, integrating additional constraints to guide the inference, both to reduce uncertainty in case of partial match and to...
Preference modelling based on Atanassov's intuitionistic fuzzy sets are gaining increasing relevance in the field of group decision making as they provide experts with a flexible and simple tool to express their preferences on a set of alternative options, while allowing, at the same time, to accommodate experts' preference uncertainty, which is inherent to all decision making processes. A key issue...
Data is frequently characterised by both uncertainty and seasonality. Type-2 fuzzy sets are an extension of type-1 fuzzy sets offering a conceptual scheme within which the effects of uncertainties in fuzzy inferencing may be modelled and minimised. Complex fuzzy sets are type-1 fuzzy sets extended by an additional phase term which permits them to intuitively represent the seasonal aspect of fuzziness...
This paper studies an uncertain time-dependent vehicle routing problem with soft time windows. A novel mathematical model which considers both transportation costs (total traveling distance and number of vehicles) and service costs (early and late arrivals) is developed, and the equations for calculating the expected total service costs are deduced under uncertainty and time-dependency. A variation...
In order to solve the problems emerged in the situation trying to combine conflict evidences using Dempster-Shafer theory, a new combination rule based on Dubois and Prade rule was raised. A method of using the new rule together with Bayesian Approximation was proposed to combine evidences from different bodies. Firstly the proposition transformation of discernment frame was discussed both in the...
In this paper, we develop the parametric graded mean integration representation (PGMIR) for the interval type 2 fuzzy sets (IT2FS). The PGMIR value can be served as the type reduced or defuzzification of an IT2FS. A set of IT2FSs can then be ordered by the corresponding PGMIR values. Firstly, the parametric representation of the embedded fuzzy sets is defined for the general IT2FS. The IT2FS is characterized...
This paper deals with observer-based controller design problem for discrete-time Linear Parameter Varying (LPV) systems with uncertain parameters. The proposed technique consists in designing an observer-based controller which stabilizes the LPV system, provided that the norm of the difference between the measured parameter and the real one does not exceed a tolerated maximum value. The particularity...
In this paper, a new robust beamforming approach which is capable of accurately controlling the main beam response is proposed. In this method, steering vector uncertainties are taken into account in the beamformer design problem with array magnitude response constraints. This allows us to control the main beam as prescribed. However, the resultant non-convex problem has a different formulation from...
Recently we developed supervisor localization, a top-down approach to distributed control of discrete-event systems. Its essence is the allocation of monolithic (global) control action among the local control strategies of individual agents. In this paper, we extend supervisor localization by considering partial observation; namely not all events are observable. Specifically, we employ the recently...
This paper aims at design and analysis of iterative learning control (ILC) for a class of nonlinear wave equations. Without any simplification or discretization of the 3D dynamics in the time, the space as well as the iteration domains, the learning convergence of ILC is analyzed for the wave equations directly by virtue of the contraction mapping methodology and λ-norm. It is shown that the proposed...
This paper studies the fixed-time stabilization problem for a class of second-order multivariable multi-input nonlinear systems with matched uncertainties. Different from the control design for scalar systems, a multivariable sliding mode control design, based on a set of nonlinear coupled sliding surfaces, is proposed to stabilize the system at the origin with a uniformly bounded settling time, i...
The drawbacks of electrical balances calculation methods are considered in the paper. In order to increase accuracy of appropriate balances it is necessary to verify them via electricity meters. Due to limited number of electricity meters and time appropriate verification can be carried out only for certain amount of technological objects. The new algorithm of identification of technological equipment,...
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