# Radial basis function

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## 노트

### 위키데이터

- ID : Q1588488

### 말뭉치

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

### 소스

- ↑ Radial Basis Function Networks - an overview
- ↑ [ Archived Post Radial Basis Function Artificial Neural Networks]
- ↑
^{3.0}^{3.1}Software Analysis Files and Solutions - ↑
^{4.0}^{4.1}Radial basis function neural networks: A topical state-of-the-art survey - ↑ Radial Basis Function Neural Networks (with parameter selection using K-means)
- ↑
^{6.0}^{6.1}^{6.2}Radial basis function network - ↑
^{7.0}^{7.1}^{7.2}Radial Basis Functions - ↑
^{8.0}^{8.1}^{8.2}Radial basis function - ↑
^{9.0}^{9.1}Using Neural Networks for Regression: Radial Basis Function Networks - ↑
^{10.0}^{10.1}^{10.2}^{10.3}Radial Basis Function Neural Network Based on an Improved Exponential Decreasing Inertia Weight-Particle Swarm Optimization Algorithm for AQI Prediction - ↑
^{11.0}^{11.1}^{11.2}^{11.3}Radial Basis Function Neural Network (RBFNN) - ↑ Radial basis function neural network path loss prediction model for LTE networks in multitransmitter signal propagation environments
- ↑
^{13.0}^{13.1}^{13.2}^{13.3}Nearest Neighbour Radial Basis Function Solvers for Deep Neural... - ↑ Improving the Generalization Properties of Radial Basis Function Neural Networks

## 메타데이터

### 위키데이터

- ID : Q1588488