Partial differential equations (PDEs) on surfaces arise in a variety of application areas including biological systems, medical imaging, fluid dynamics, mathematical physics, image processing and computer graphics. In this paper, we propose a radial basis function (RBF) discretization of the closest point method. The corresponding localized meshless method may be used to approximate diffusion o...

Incremental Net Pro (IncNet Pro) with local learning feature and statistically controlled growing and pruning of the network is introduced. The architecture of the net is based on RBF networks. Extended Kalman Filter algorithm and its new fast version is proposed and used as learning algorithm. IncNet Pro is similar to the Resource Allocation Network described by Platt in the main idea of the e...

A Radial Basis Function ( RBF ) neural network can be regarded as a feed forward network composed of multiple layers of neurons with entirely different roles. The input layer made of sensory units that connect the network to its environment. A radial basis function neural network depends mainly upon an adequate choice of the number and positions of its basis function centers. In case of general...

Abstract: Incremental Net Pro (IncNet Pro) with local learning feature and statistically controlled growing and pruning of the network is introduced. The architecture of the net is based on RBF networks. Extended Kalman Filter algorithm and its new fast version is proposed and used as learning algorithm. IncNet Pro is similar to the Resource Allocation Network described by Platt in the main ide...

Incremental Net Pro IncNet Pro with local learning feature and statistically controlled growing and pruning of the network is intro duced The architecture of the net is based on RBF networks Extended Kalman Filter algorithm and its new fast version is proposed and used as learning algorithm IncNet Pro is similar to the Resource Allocation Network described by Platt in the main idea of the expan...

| Conventional speech recognition systems based on Multi Layer Percep-trons often use Time Delay Neural Networks (TDNN). TDNNs were rst used for speech recognition by Waibel et al., but long training times and large numbers of parameters that need careful adjustment make it hard to achieve good performance. In contrast, networks using Radial Basis Functions (RBF) can be constructed systematical...

In this paper we improve the cubature rules discussed in Sommariva and Vianello (2021) for computation of integrals by radial basis functions (RBFs). More precisely, introduce context meshless a leave-one-out cross validation criterion optimization RBF shape parameter. This choice allows us to get highly reliable accurate results any kind both infinity finite regularity RBF. The efficacy approx...

A Radial Basis Function ( RBF ) neural network can be regarded as a feed forward network composed of multiple layers of neurons with entirely different roles. The input layer made of sensory units that connect the network to its environment. A radial basis function neural network depends mainly upon an adequate choice of the number and positions of its basis function centers. In case of general...

A Radial Basis Function ( RBF ) neural network can be regarded as a feed forward network composed of multiple layers of neurons with entirely different roles. The input layer made of sensory units that connect the network to its environment. A radial basis function neural network depends mainly upon an adequate choice of the number and positions of its basis function centers. In case of general...

In this paper we discuss Sobolev bounds on functions that vanish at scattered points in a bounded, Lipschitz domain that satisfies a uniform interior cone condition. The Sobolev spaces involved may have fractional as well as integer order. We then apply these results to obtain estimates for continuous and discrete least squares surface fits via radial basis functions (RBFs). These estimates inc...