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Adaptive dynamical systems arise in a multitude of contexts, e.g., optimization, control, communications, signal processing, and machine learning. A precise characterization of their fundamental limitations is therefore of paramount importance. In this paper, we consider the general problem of adaptively controlling and/or identifying a stochastic dynamical system, where our a priori knowledge allows...
This paper is concerned with optimization of stochastic uncertain systems, when systems are described by measures and the pay-off by a linear functional on the space of measure, on general abstract spaces. Robustness is formulated as a minimax game, in which the control seeks to minimize the pay-off over the admissible controls while the measure aims at maximizing the pay-off over the total variational...
This paper addresses the decision trees induction with uncertain data. In other words, it presents a novel method, called uncertain decision trees (UDT) to handle the uncertainty during the process of inducing decision trees. Here, uncertainty is depicted via cloud model theory, a quantitative-qualitative transforming model with uncertainty, which can well integrate the fuzziness and randomness of...
This paper presents two new approaches that enable the use of linear landmarks for planning paths with uncertainty in position in outdoor environments. The first approach uses a combination of forward simulation and entropy to reduce the dimensionality of the search space, while still preserving most of the information required to propagate a full covariance matrix. The second approach adds incremental...
Because there are lots of random and fuzzy uncertainty factors in power system, the certainty model used to be adopted in reliability research were not reasonable. But cloud models theory is a powerful tool to convert numerical quantitative analysis to conceptual qualitative analysis. In this paper on the basis of introduction of cloud models, the parameter and load cloud models in actual operation...
Traditional decision tree classifiers work with data whose values are known and precise. We extend such classifiers to handle data with uncertain information, which originates from measurement/quantisation errors, data staleness, multiple repeated measurements, etc. The value uncertainty is represented by multiple values forming a probability distribution function (pdf). We discover that the accuracy...
It's the emphasis and difficulty to understand various uncertain information effectively and depict it exactly in the flexible decision making process. The set pair analysis is a useful approach to analyze and research uncertain system. In this paper, it takes trigonometry fuzzy numbers to describe the identity, discrepancy and contrary degree of the connection numbers, and presents construction method...
In order to give an information theoretic interpretation to Bode integral of continuous time linear systems, the epsilon-entropy rate is adopted to measure time average uncertainty of continuous time stationary processes. The variety of system sensitivity function defined as the difference between epsilon-entropy rates of disturbance input and system output formulates how does the uncertainty of disturbance...
Successful negotiators look beyond a purely utilitarian view. We propose a new agent architecture that was inspired by the observation that "Everything that an agent says gives away (valuable) information.'' It is intended for agents who are uncertain about their environment. Information-based agency uses tools from information theory, and includes techniques for managing information exchange...
In this paper, we study the problem of stabilizing a linear time-invariant discrete-time system with information constraints in the input channels. The information constraint in each input channel is modeled as a sector uncertainty. Equivalently, the transmission error of an input channel is modeled as an additive system uncertainty with a bound in the induced norm.We attempt to find the least information...
In this paper entropy based methods are compared and used to measure structural diversity of an ensemble of 21 classifiers. This measure is mostly applied in ecology, whereby species counts are used as a measure of diversity. The measures used were Shannon entropy, Simpsons and the Berger Parker diversity indexes. As the diversity indexes increased so did the accuracy of the ensemble. An ensemble...
Uncertainty is an essential characteristic of intelligence. It has various types and usually each type can be measured in a different way. Thus, it is not helpful for thinking and processing as a whole. In this paper, based on entropy, we discuss the measurement of four common types of uncertainty: randomness, fuzziness, contradiction and dynamic. We present the concept of uncertain entropy and analyze...
Uncertainty measure is a key issue of uncertain systems. In this paper, conditional entropy and increment information are introduced to discuss the two set relations in the same incomplete information systems based on covering approximation space. Two new kinds of measurements about the knowledge of rough set are presented. Furthermore the finer the set is, the lower the conditional entropy and increment...
This paper deals with the degree of uncertainty associated with fuzzy variables. Based on the notion of credibility measure, a definition of entropy is formulated from an information theoretical view and its properties are investigated. Finally, some comments are given on the construction of alternative definitions of entropy and axiomatic characterization.
Fuzzy entropy is an important concept of intuitionistic fuzzy sets (IFSs). In this paper, the resources of the entropy of an intuitionistic fuzzy set are analyzed. It is pointed out that the fuzzy entropy of an IFS comes from uncertainty and unknown information. A new formula is proposed and some numerical examples are given to compare it with the existing methods. It is found that conditions proposed...
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