Neural-like computing with populations of superparamagnetic basis functions
نویسندگان
چکیده
منابع مشابه
MQ-Radial Basis Functions Center Nodes Selection with PROMETHEE Technique
In this paper, we decide to select the best center nodes of radial basis functions by applying the Multiple Criteria Decision Making (MCDM) techniques. Two methods based on radial basis functions to approximate the solution of partial differential equation by using collocation method are applied. The first is based on the Kansa's approach, and the second is based on the Hermit...
متن کاملComputing with Pavlovian Populations
Population protocols have been introduced by Angluin et al. as a model of networks consisting of very limited mobile agents that interact in pairs but with no control over their own movement. A collection of anonymous agents, modeled by finite automata, interact pairwise according to some rules that update their states. Predicates on the initial configurations that can be computed by such proto...
متن کاملComputing Eigenmodes of Elliptic Operators Using Radial Basis Functions
K e y w o r d s Radial basis functions, Eigenvalues, Numerical methods, Laplacian, corner singularities. 1. I N T R O D U C T I O N Many positive properties of radial basis function (RBF) methods have been identified in connection with boundary-value problems (BVPs) [1-4]. They are grid-free numerical schemes very suitable for problems in irregular geometries. They can exploit accurate and smoo...
متن کاملOptimal snapshot location for computing POD basis functions
The construction of reduced order models for dynamical systems using proper orthogonal decomposition (POD) is based on the information contained in so-called snapshots. These provide the spatial distribution of the dynamical system at discrete time instances. This work is devoted to optimizing the choice of these time instances in such a manner that the error between the POD-solution and the tr...
متن کاملIdentification of Wiener Model Using Radial Basis Functions Neural Networks
A new method is introduced,for the identification of Wiener model. The Wiener model consists of a linear,dynamic! block followed by a static nonlinearity. The nonlinearity and the linear dynamic part in the model are identified by using radial basis functions neural network (RBFNN) and autoregressive moving average (ARMA) model, respectively. The new algorithm makes use of the well known mappin...
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: Nature Communications
سال: 2018
ISSN: 2041-1723
DOI: 10.1038/s41467-018-03963-w