Radial basis function

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• ID : Q1588488

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1. A radial basis function (RBF) network has been suggested as one of the most suitable multilayer network algorithms, quick to train and efficient to map any nonlinear input–output relationships.
2. → output is a real value → each neuron have Radial Basis function → centred on the point of the same dimension.
3. The radial basis function for a neuron has a center and a radius (also called a spread).
4. Each neuron consists of a radial basis function centered on a point with as many dimensions as there are predictor variables.
5. N2 - Radial basis function networks (RBFNs) have gained widespread appeal amongst researchers and have shown good performance in a variety of application domains.
6. AB - Radial basis function networks (RBFNs) have gained widespread appeal amongst researchers and have shown good performance in a variety of application domains.
7. Optimized K-means Segmentation and Radial Basis Function Neural Networks,” International Journal of Information and Communication Technology Research (IJICT), vol.
8. In the field of mathematical modeling, a radial basis function network is an artificial neural network that uses radial basis functions as activation functions.
9. Radial basis function networks have many uses, including function approximation, time series prediction, classification, and system control.
10. Functions that depend only on the distance from a center vector are radially symmetric about that vector, hence the name radial basis function.
11. A Radial basis function is a function whose value depends only on the distance from the origin.
12. A Radial basis function works by defining itself by the distance from its origin or center.
13. The Gaussian variation of the Radial Basis Function, often applied in Radial Basis Function Networks, is a popular alternative.
14. Error estimates for matrix-valued radial basis function interpolation Journal of Approximation Theory 137: 234-249.
15. Sobolev bounds on functions with scattered zeros, with applications to radial basis function surface fitting Mathematics of Computation 74: 643-763.
16. Local error estimates for radial basis function interpolation of scattered data IMA Journal of Numerical Analysis 13: 13-27.
17. Radial Basis Function Networks (RBF nets) are used for exactly this scenario: regression or function approximation.
18. They are similar to 2-layer networks, but we replace the activation function with a radial basis function, specifically a Gaussian radial basis function.
19. Each hidden neuron has a radial basis function which is a center symmetric nonlinear function with local distribution.
20. The radial basis function consists of a center position and a width parameter.
21. is Euclidean norm usually taking 2-norm. is the radial basis function.
22. Based on the EDIW-PSO algorithm, we optimize the centers, widths, and connection weights of radial basis function (RBF) neural network.
23. This paper proposed a radial basis function (RBF) neural network method to forecast the wind power generation of WECS.
24. Three parameterize RBFNs model with the centers and spreads of each radial basis function, and the connection weights to solve the mobile robot path traveling and routing problems.
25. A major kind of neural network, i.e. radial basis function neural network (RBFNN), is used to model the fault diagnosis structure.
26. A new algorithm for training radial basis function neural network (RBFNN) is presented in this paper.
27. The developed path loss prediction models are the radial basis function neural network (RBFNN) and the multilayer perception neural network (MLPNN).
28. We present a radial basis function solver for convolutional neural networks that can be directly applied to both distance metric learning and classification problems.
29. Our method treats all training features from a deep neural network as radial basis function centres and computes loss by summing the influence of a feature's nearby centres in the embedding space.
30. Having a radial basis function centred on each training feature is made scalable by treating it as an approximate nearest neighbour search problem.
31. We show that our radial basis function solver outperforms state-of-the-art embedding approaches on the Stanford Cars196 and CUB-200-2011 datasets.
32. An important feature of radial basis function neural networks is the existence of a fast, linear learning algorithm in a network capable of representing complex nonlinear mappings.