Set classification aims to classify a set of observations as whole, opposed classifying individual separately. To formally understand the unfamiliar concept binary classification, we first investigate optimal decision rule under normal distribution, which utilizes empirical covariance be classified. We show that number in plays critical role bounding Bayes risk. Under this framework, further pr...

Leveraging autonomous systems in safety-critical applications requires formal robustness guarantees against uncertainties. We address this issue by computing safe terminal sets with corresponding safety-preserving controllers, which ensure robust constraint satisfaction for an infinite time horizon. To maximize the region of operation, we also construct as large possible initial that can be saf...

kim and bishu (fuzzy sets and systems 100 (1998) 343-352) proposeda modification of fuzzy linear regression analysis. their modificationis based on a criterion of minimizing the difference of the fuzzy membershipvalues between the observed and estimated fuzzy numbers. we show that theirmethod often does not find acceptable fuzzy linear regression coefficients andto overcome this shortcoming, pr...

In this paper we upgraded the Luhandjula’s method (Fuzzy Sets and Systems, 13(1), (1984), 11-23) for multiple objective linear plus fractional programming problems (MOL+FPP) with modification given by Dutta et al. (Fuzzy Sets and System, 52(1), (1992), 39-45). The aim of this paper is to show new fuzzy set approach for MOL+FPP by defining new membership function for linear function part and si...

This paper presents a novel adaptive neuro-fuzzy inference system based on interval Gaussian type-2 fuzzy sets in the antecedent part and Gaussian type-1 fuzzy sets as coefficients of linear combination of input variables in the consequent part. The capability of the proposed ANFIS2 for function approximation and dynamical system identification is remarkable. The structure of ANFIS2 is very sim...