ICAR: endoscopic skull‐base surgery

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Intuitionistic Fuzzy Time Series Functions Approach for Time Series Forecasting

Fuzzy inference systems have been commonly used for time series forecasting in the literature. Adaptive network fuzzy inference system, fuzzy time series approaches and fuzzy regression functions approaches are popular among fuzzy inference systems. In recent years, intuitionistic fuzzy sets have been preferred in the fuzzy modeling and new fuzzy inference systems have been proposed based on intuitionistic fuzzy sets. In this paper, a new intuitionistic fuzzy regression functions approach is proposed based on intuitionistic fuzzy sets for forecasting purpose. This new inference system is called an intuitionistic fuzzy time series functions approach. The contribution of the paper is proposing a new intuitionistic fuzzy inference system. To evaluate the performance of intuitionistic fuzzy time series functions, twenty-three real-world time series data sets are analyzed. The results obtained from the intuitionistic fuzzy time series functions approach are compared with some other methods according to a root mean square error and mean absolute percentage error criteria. The proposed method has superior forecasting performance among all methods

The Equivalence Structure of Descriptor Representations of Systems with Possibly Inconsistent Initial Conditions

Necessary and sufficient conditions for minimality of descriptor representations of impulsivesmooth behaviors are derived. We obtain a complete set of transformations by which minimal descriptor representations that give rise to the same behavior can be transformed into each other. In particular this leads to a jump-behavioral interpretation of the notion of strong equivalence of descriptor representations. 1 Introduction What is a linear system? During the past decades, this rather basic question has been answered in remarkably many ways. In the standard state space framework which has dominated large parts of modern linear system theory, a linear system is essentially a four-tuple of matrices (A; B; C; D) modulo the similarity transformation (A; B; C; D) ! (SAS \Gamma1 ; SB;CS \Gamma1 ; D). For some purposes however this setting is not sufficiently general. In particular, the modeling of systems that switch between different operating regimes ("hybrid" systems) calls for a sys..