[no abstract available]

in this paper, we use parametric form of fuzzy number and we converta fuzzy linear system to two linear system in crisp case. conditions for the existence of a minimal solution to $mtimes n$ fuzzy linear equation systems are derived and a numerical procedure for calculating the minimal solution is designed. numerical examples are presented to illustrate the proposed method.

in this paper a new algorithm is presented for coherent radar detection of targets, when distribution of the interference (clutter and noise) is non-gaussian. the neyman-pearson criterion is used for optimal detection and several successive received samples from a radar range cell are used for detection in the same cell. on the basis of recent empirical evidences, the interference distribution ...

In this paper, a speech classifier inspired by the signal subspace approach is developed. A novel signal subspace speech model is initially obtained via a rank reducing subspace decomposition algorithm that is based on the SVD. Motivated by the assumption that the speech signal comprises of short term dynamics that are slowly changing, it follows that the signal subspace of the speech signal is...

(∆− λ)f = T for a Γ-invariant distribution T on H, compactly-supported on Γ\H. The three main examples for T are described in detail below. That is, a slight roughness is tolerated in f at the support of the Γ-invariant distribution T . Three natural types of target distributions T are: (i) T is supported at a single point on Γ\H, (ii) T is supported on a closed geodesic on Γ\H, (iii) T evaluat...

The subspace-based methods are effectively applied to classify sets of feature vectors by modeling them as subspaces. It is, however, difficult to appropriately determine the subspace dimensionality in advance for better performance. For alleviating such issue, we present a generalized mutual subspace method by introducing soft weighting across the basis vectors of the subspace. The bases are e...

We present a framework for Tensor-based subspace Tracking via Kronecker-structured projections (TeTraKron). TeTraKron allows to extend arbitrary matrix-based subspace tracking schemes to track the tensor-based subspace estimate. The latter can be computed via a structured projection applied to the matrix-based subspace estimate which enforces the multi-dimensional structure in a computationally...

This paper presents a new method for text independent speaker recognition. We embed both training and test sessions into a session space. The session space is a direct sum of a common-speaker subspace and a speaker-unique subspace. The common-speaker subspace is Euclidean and is spanned by a set of reference sessions. Kernel-PCA is used to explicitly embed sessions into the common-speaker subsp...

Projection pursuit is concerned with finding interesting lowdimensional subspace of multivariate data. In this paper we proposed a genetic optimization approach to find the globally optimal orthogonal subspace given training data and user defined criterion on what subspaces are interesting. We then applied this approach to human face recognition. Suppose face recognition is done by simple corre...