A conformable calculus of radial basis functions and its applications
نویسندگان
چکیده
منابع مشابه
A Hybrid Radial Basis Function Neurocomputer and Its Applications
A neurocomputer was implemented using radial basis functions and a combination of analog and digital VLSI circuits. The hybrid system uses custom analog circuits for the input layer and a digital signal processing board for the hidden and output layers. The system combines the advantages of both analog and digital circuits. featuring low power consumption while minimizing overall system error. ...
متن کاملMatrix-valued radial basis functions: stability estimates and applications
Radial basis functions (RBFs) have found important applications in areas such as signal processing, medical imaging, and neural networks since the early 1980’s. Several applications require that certain physical properties are satisfied by the interpolant, for example being divergence free in case of incompressible data. In this paper we consider a class of customized (e.g. divergence-free) RBF...
متن کاملRadial basis functions: Developments and applications to planetary scale flows
0045-7930/$ see front matter 2010 Elsevier Ltd. A doi:10.1016/j.compfluid.2010.08.005 * Corresponding author. E-mail addresses: [email protected] (N. Flyer), fornber Radial basis functions (RBFs) can be seen as a major generalization of pseudospectral (PS) methods, abandoning the orthogonality of the basis functions and in return obtaining much improved simplicity and geometric flexibility. Spectr...
متن کاملRadial basis functions
Radial basis function methods are modern ways to approximate multivariate functions, especially in the absence of grid data. They have been known, tested and analysed for several years now and many positive properties have been identified. This paper gives a selective but up-to-date survey of several recent developments that explains their usefulness from the theoretical point of view and contr...
متن کاملDeformable Radial Basis Functions
Radial basis function networks (RBF) are efficient general function approximators. They show good generalization performance and they are easy to train. Due to theoretical considerations RBFs commonly use Gaussian activation functions. It has been shown that these tight restrictions on the choice of possible activation functions can be relaxed in practical applications. As an alternative differ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: An International Journal of Optimization and Control: Theories & Applications (IJOCTA)
سال: 2018
ISSN: 2146-5703,2146-0957
DOI: 10.11121/ijocta.01.2018.00544