In this paper we introduce the use of contextual transformation functions to adjust membership functions in fuzzy systems. We address both linear and nonlinear functions to perform linear or nonlinear context adaptation, respectively. The key issue is to encode knowledge in a standard frame of reference, and have its meaning tuned to the situation by means of an adequate transformation reflecting the influence of context in the interpretation of a concept. Linear context adaptation is simple and fast. Nonlinear context adaptation is more computationally expensive, but due to its nonlinear characteristic, different parts of base membership functions can be stretched or expanded to best fit the desired format. Here we use a genetic algorithm to find a nonlinear transformation function, given the base membership functions and a set of data extracted from the environment classified by means of fuzzy concepts. © 1998 John Wiley & Sons, Inc.
This study is concerned with the concept of information granularity, its representation and use along with a discussion on selected application areas. We discuss several key methodologies involved therein with a particular focus on fuzzy set technology. The agenda of the paper embraces two key issues: (i) underlying fundamentals of information granularity and various ways of processing of information granules and (ii) the use of the methodology of granular computing to a broad range of problems of system modeling, control, and classification. Activities carried out under the auspices of fuzzy sets (fuzzy modeling) as well as data mining and neural networks exploit the ideas of granular computing. We discuss them in more detail highlighting their advantages and design practices.
As a new and meaningful extension of the Pawlak rough set, multi-granulation rough sets (MGRSs) have attracted much attention and fruitful achievements have been reported in different aspects. By combining with fuzzy rough set, the paper introduces multi-granulation fuzzy rough sets in the covering approximation space, namely, covering-based multi-granulation fuzzy rough sets (CMFRS), which form the extension of fuzzy rough sets. We first investigate several important properties of lower and upper approximations of concepts in covering-based multi-granulation fuzzy rough sets and elaborate on the differences between the proposed models and the existing ones in literature. By employing the notions of reduct and exclusion of a covering, the paper studies the necessary and sufficient conditions for two CMFRS to generate identical lower and upper approximations of a target concept in the given covering approximation space. Finally, the relationships between the new models are explored in the paper.
The ubiquitous presence of the Internet creates new opportuni-ties for component distribution. Infrastructures for dynamic, web-based composition of software components appears to be greatly-needed. WebCODS targets this need: it is a web-based system that supports dynamic component composition over the web. This article discusses the component composition aspects of WebCODS. Specific attention is devoted to the model of composition, the inter-connection between components, and the implementation strategy. An example is presented.
Linguistic fuzzy information evolution is crucial in understanding information exchange among agents. However, different agent weights may lead to different convergence results in the classic DeGroot model. Similarly, in the Hegselmann-Krause bounded confidence model (HK model), changing the confidence threshold values of agents can lead to differences in the final results. To address these limitations, this paper proposes three new models of linguistic fuzzy information dynamics: the per-round random leader election mechanism-based DeGroot model (PRRLEM-DeGroot), the PRRLEM-based homogeneous HK model (PRRLEM-HOHK), and the PRRLEM-based heterogeneous HK model (PRRLEM-HEHK). In these models, after each round of fuzzy information updates, an agent is randomly selected to act as a temporary leader with more significant influence, with the leadership structure being reset after each update. This strategy increases the information sharing and enhances decision-making by integrating multiple agents' evaluation information, which is also in line with real life (\emph{Leader is not unchanged}). The Monte Carlo method is then employed to simulate the behavior of complex systems through repeated random tests, obtaining confidence intervals for different fuzzy information. Subsequently, an improved golden rule representative value (GRRV) in fuzzy theory is proposed to rank these confidence intervals. Simulation examples and a real-world scenario about space situational awareness validate the effectiveness of the proposed models. Comparative analysis with the other models demonstrate our ability to address the echo chamber and improve the robustness.
Atmospheric electric field signal (AEFS) features can be characterized by their average value (AV), standard deviation (SD), and entropy value (EV). How to mine and fully utilize AEFS features to ensure reliable and efficient thunderstorm detection has not been considered so far. In this article, based on the stacked autoencoder (SAE) and extreme gradient boosting (XGBoost) model, extracted deep-seated features of AEFS are used to obtain its predicted value (PV). It fuses three regular features plus one PV feature and proposes a thunderstorm moving path (TMP) prediction system with switchable patterns among the applied three AEFS prediction models based on the convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM). This fully considers that a single model is difficult to meet AEFS predictions with different weather attributes. Specifically, AEF data measured by a self-made AEF apparatus are adopted to determine feature values (FVs). According to FV intervals (FVIs) in sunny and thunderstorm weathers, the proportion of each feature satisfying FVIs is taken as the weighting factors of corresponding feature terms. A switchable pattern function with different switching conditions is formed by combining weightings and feature variables. Optimal AEFS prediction models are fixed under the same switching condition and applied to corresponding patterns. Empirical results confirm that the proposed system effectively predicts TMPs, with an average determination coefficient of 95.58%. This is the first study to design switchable patterns to detect thunderstorms from a new perspective of multiple AEFS feature fusion, which provides promising solutions to the refinement and intelligent prediction of thunderstorms.
In this study, we develop a process of estimation of importance of features considered in face recognition by making use of the analytic hierarchy process (AHP). The AHP method of pairwise comparisons realized at three levels of hierarchy becomes crucial to realize a comprehensive weighting of cues so that sound estimates of weights associated with the individual features of faces can be formed. We demonstrate how to carry out an efficient process of face description by using a collection of linguistic descriptors of the features and their groups. Numerical dependencies between the features are quantified with the help of experienced criminology and psychology experts. Finally, we present an entropy-based method of evaluation of the relevance of the estimation process completed by the individuals. The intuitively appealing results of experiments are presented and analyzed in detail.
