This paper presents a new perception-based computing approach in a wall-following algorithm. The proposed perception-based computing uses a rough-fuzzy theory, which is an extension of the conventional fuzzy-based con...
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(纸本)9781479960811
This paper presents a new perception-based computing approach in a wall-following algorithm. The proposed perception-based computing uses a rough-fuzzy theory, which is an extension of the conventional fuzzy-based control approach. In practice, an indoor robot follows a wall in a compacted and complex environment with limited acquired data. Furthermore, visual sensor measurements may contain errors in a number of situations. In order to improve uncertainty reasoning results, it is necessary to perceive the encountered environment and filter the measured data. Therefore, a rough set theory is integrated to extract essential features of data to regulate inputs before applying fuzzy inference rules. The proposed control algorithm demonstrates excellent results through simulation and implementation.
This paper aims to shed light on some of the conceptual interpretations that might be extracted from either the information conveyed by fuzzy differential equations (FDEs) or a fusion of such information with other da...
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This paper aims to shed light on some of the conceptual interpretations that might be extracted from either the information conveyed by fuzzy differential equations (FDEs) or a fusion of such information with other data sources. Although no explicit effort has been conducted on the topic, there are two significant imperatives for such interpretations. First, conceptual interpretations are necessary when FDEs are employed in modelling, prediction, control theory, and suchlike subjects. Simply put, FDEs fail to be applicable if their corresponding interpretations have not been recognized conceptually. Second, when FDEs are supposed to be analyzed. In other words, in the lack of such interpretations of FDEs, any analysis on this type of differential equations is conceptually devoid of meaning. The interpretations are associated with the concept of possibility distribution and differential equations which might be called imprecise differential equations (IDEs) that are in effect the mother of differential equations. The concept of IDE makes a convenient point of departure for the development of the interpretations in question in terms of the concepts such as machine-oriented meaning precisiation, modal solutions, precisiations compatibility, and some new facets of sureness. Specifically, interpretations associated with the compatibility of precisiations and the notion of human-machine-based sureness demonstrate the role played by information resident in an FDE fused with perception-based meaning precisiation, that stems from the knowledge of an expert. Some illustrations and examples are presented to clarify the basic concepts and the applications of interpretations.
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