neural networks are applicable in identification systems from input-output data. in this report, we analyze thehammerstein-wiener models and identify them. thehammerstein-wiener systems are the simplest type of block orientednonlinear systems where the linear dynamic block issandwiched in between two static nonlinear blocks, whichappear in many engineering applications; the aim of nonlinearsyst...

This study develops and analyzes preconditioned Krylov subspace methods to solve linear systems arising from discretization of the time-independent space-fractional models. First, we apply shifted Grunwald formulas to obtain a stable finite difference approximation to fractional advection-diffusion equations. Then, we employee two preconditioned iterative methods, namely, the preconditioned gen...

In [1,2,3], A. C. Baker and J.W. Baker studied the subspace Ma(S) of the convolution measure algebra M, (S) of a locally compact semigroup. H. Dzinotyiweyi in [5,7] considers an analogous measure space on a large class of C-distinguished topological semigroups containing all completely regular topological semigroups. In this paper, we extend the definitions to study the weighted semigroup ...

Often, we work with vector spaces which consists of an appropriate. Many concepts concerning vectors in Rn can be extended to other mathematical systems. We can think of a vector space in.of a finite-dimensional nontrivial proper subspace of such a vector space is equivalent to ACA0 over RCA0. This paper is a continuation of 3.Subspaces of Vector Spaces. A subspace W of a vector space V is a su...

Hyperspectral image (HSI) clustering is a challenging task due to the high complexity of HSI data. Subspace has been proven be powerful for exploiting intrinsic relationship between data points. Despite impressive performance in clustering, traditional subspace methods often ignore inherent structural information among In this article, we revisit with graph convolution and present novel framewo...

This paper presents a new subspace modeling and selection approach for noisy speech recognition. In subspace modeling, we develop factor analysis (FA) for representing noisy speech. FA is a data generation model where the common factors are extracted with factor loading matrix and specific factors. We bridge the connection of FA to signal subspace (SS) approach. Interestingly, FA partitions noi...