Researchers at the University of California, Los Angeles (UCLA) have developed an optical computing framework that performs large-scale nonlinear computations using linear materials. Reported in ...
Abstract: In this paper, a successive radial basis function (RBF) approximation approach is proposed to solve the Hamilton-Jacobi-Isaacs (HJI) partial differential equation (PDE) associated with ...
ABSTRACT: We solve numerically an eigenvalue elliptic partial differential equation (PDE) ranging from two to six dimensions using the generalized multiquadric (GMQ) radial basis functions (RBFs). Two ...
Abstract: The passive intermodulation (PIM) interference in simultaneous transmit-receive system is difficult to deal with because the process of generating PIM signal is both nonlinear and with ...
Radial Basis Function Neural Networks (RBFNNs) are a type of neural network that combines elements of clustering and function approximation, making them powerful for both regression and classification ...
Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. The assumptions underpinning the adiabatic Born–Oppenheimer (BO) approximation are ...
Resistant training in radial basis function (RBF) networks is the topic of this paper. In this paper, one modification of Gauss-Newton training algorithm based on the theory of robust regression for ...
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