Radial basis function

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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.[1]
  2. → output is a real value → each neuron have Radial Basis function → centred on the point of the same dimension.[2]
  3. The radial basis function for a neuron has a center and a radius (also called a spread).[3]
  4. Each neuron consists of a radial basis function centered on a point with as many dimensions as there are predictor variables.[3]
  5. N2 - Radial basis function networks (RBFNs) have gained widespread appeal amongst researchers and have shown good performance in a variety of application domains.[4]
  6. AB - Radial basis function networks (RBFNs) have gained widespread appeal amongst researchers and have shown good performance in a variety of application domains.[4]
  7. Optimized K-means Segmentation and Radial Basis Function Neural Networks,” International Journal of Information and Communication Technology Research (IJICT), vol.[5]
  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.[6]
  9. Radial basis function networks have many uses, including function approximation, time series prediction, classification, and system control.[6]
  10. Functions that depend only on the distance from a center vector are radially symmetric about that vector, hence the name radial basis function.[6]
  11. A Radial basis function is a function whose value depends only on the distance from the origin.[7]
  12. A Radial basis function works by defining itself by the distance from its origin or center.[7]
  13. The Gaussian variation of the Radial Basis Function, often applied in Radial Basis Function Networks, is a popular alternative.[7]
  14. Error estimates for matrix-valued radial basis function interpolation Journal of Approximation Theory 137: 234-249.[8]
  15. Sobolev bounds on functions with scattered zeros, with applications to radial basis function surface fitting Mathematics of Computation 74: 643-763.[8]
  16. Local error estimates for radial basis function interpolation of scattered data IMA Journal of Numerical Analysis 13: 13-27.[8]
  17. Radial Basis Function Networks (RBF nets) are used for exactly this scenario: regression or function approximation.[9]
  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.[9]
  19. Each hidden neuron has a radial basis function which is a center symmetric nonlinear function with local distribution.[10]
  20. The radial basis function consists of a center position and a width parameter.[10]
  21. is Euclidean norm usually taking 2-norm. is the radial basis function.[10]
  22. Based on the EDIW-PSO algorithm, we optimize the centers, widths, and connection weights of radial basis function (RBF) neural network.[10]
  23. This paper proposed a radial basis function (RBF) neural network method to forecast the wind power generation of WECS.[11]
  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.[11]
  25. A major kind of neural network, i.e. radial basis function neural network (RBFNN), is used to model the fault diagnosis structure.[11]
  26. A new algorithm for training radial basis function neural network (RBFNN) is presented in this paper.[11]
  27. The developed path loss prediction models are the radial basis function neural network (RBFNN) and the multilayer perception neural network (MLPNN).[12]
  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.[13]
  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.[13]
  30. Having a radial basis function centred on each training feature is made scalable by treating it as an approximate nearest neighbour search problem.[13]
  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.[13]
  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.[14]