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# This article assumes you have advanced programming skills with C# and a basic familiarity with the radial basis function network input-process-output mechanism.<ref name="ref_9b003e8e" />
 
# This article assumes you have advanced programming skills with C# and a basic familiarity with the radial basis function network input-process-output mechanism.<ref name="ref_9b003e8e" />
 
# Using the distance value by itself is called a linear radial basis function or linear RBF.<ref name="ref_0c7a4eca">[https://www.chadvernon.com/blog/rbf/ Regularized Linear Regression with Radial Basis Functions]</ref>
 
# Using the distance value by itself is called a linear radial basis function or linear RBF.<ref name="ref_0c7a4eca">[https://www.chadvernon.com/blog/rbf/ Regularized Linear Regression with Radial Basis Functions]</ref>
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===소스===
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<references />
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== 노트 ==
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===위키데이터===
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* ID :  [https://www.wikidata.org/wiki/Q1588488 Q1588488]
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===말뭉치===
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# 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.<ref name="ref_a8dd901e">[https://www.sciencedirect.com/topics/chemical-engineering/radial-basis-function-networks Radial Basis Function Networks - an overview]</ref>
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# → output is a real value → each neuron have Radial Basis function → centred on the point of the same dimension.<ref name="ref_070355aa">[https://medium.com/@SeoJaeDuk/archived-post-radial-basis-function-artificial-neural-networks-9fad1479a9bc [ Archived Post ] Radial Basis Function Artificial Neural Networks]</ref>
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# The radial basis function for a neuron has a center and a radius (also called a spread).<ref name="ref_406915b9">[https://www.dtreg.com/solution/view/25 Software Analysis Files and Solutions]</ref>
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# Each neuron consists of a radial basis function centered on a point with as many dimensions as there are predictor variables.<ref name="ref_406915b9" />
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# N2 - Radial basis function networks (RBFNs) have gained widespread appeal amongst researchers and have shown good performance in a variety of application domains.<ref name="ref_5409f467">[https://yonsei.pure.elsevier.com/en/publications/radial-basis-function-neural-networks-a-topical-state-of-the-art- Radial basis function neural networks: A topical state-of-the-art survey]</ref>
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# AB - Radial basis function networks (RBFNs) have gained widespread appeal amongst researchers and have shown good performance in a variety of application domains.<ref name="ref_5409f467" />
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# Optimized K-means Segmentation and Radial Basis Function Neural Networks,” International Journal of Information and Communication Technology Research (IJICT), vol.<ref name="ref_d69d5ddd">[https://www.mathworks.com/matlabcentral/fileexchange/52580-radial-basis-function-neural-networks-with-parameter-selection-using-k-means Radial Basis Function Neural Networks (with parameter selection using K-means)]</ref>
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# In the field of mathematical modeling, a radial basis function network is an artificial neural network that uses radial basis functions as activation functions.<ref name="ref_d784378a">[https://en.wikipedia.org/wiki/Radial_basis_function_network Radial basis function network]</ref>
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# Radial basis function networks have many uses, including function approximation, time series prediction, classification, and system control.<ref name="ref_d784378a" />
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# Functions that depend only on the distance from a center vector are radially symmetric about that vector, hence the name radial basis function.<ref name="ref_d784378a" />
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# A Radial basis function is a function whose value depends only on the distance from the origin.<ref name="ref_5f1e1abb">[https://deepai.org/machine-learning-glossary-and-terms/radial-basis-function Radial Basis Functions]</ref>
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# A Radial basis function works by defining itself by the distance from its origin or center.<ref name="ref_5f1e1abb" />
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# The Gaussian variation of the Radial Basis Function, often applied in Radial Basis Function Networks, is a popular alternative.<ref name="ref_5f1e1abb" />
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# Error estimates for matrix-valued radial basis function interpolation Journal of Approximation Theory 137: 234-249.<ref name="ref_f77dadaa">[http://www.scholarpedia.org/article/Radial_basis_function Radial basis function]</ref>
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# Sobolev bounds on functions with scattered zeros, with applications to radial basis function surface fitting Mathematics of Computation 74: 643-763.<ref name="ref_f77dadaa" />
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# Local error estimates for radial basis function interpolation of scattered data IMA Journal of Numerical Analysis 13: 13-27.<ref name="ref_f77dadaa" />
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# Radial Basis Function Networks (RBF nets) are used for exactly this scenario: regression or function approximation.<ref name="ref_5b0e9ffb">[https://pythonmachinelearning.pro/using-neural-networks-for-regression-radial-basis-function-networks/ Using Neural Networks for Regression: Radial Basis Function Networks]</ref>
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# They are similar to 2-layer networks, but we replace the activation function with a radial basis function, specifically a Gaussian radial basis function.<ref name="ref_5b0e9ffb" />
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# Each hidden neuron has a radial basis function which is a center symmetric nonlinear function with local distribution.<ref name="ref_9a81cd99">[https://www.hindawi.com/journals/aaa/2014/178313/ Radial Basis Function Neural Network Based on an Improved Exponential Decreasing Inertia Weight-Particle Swarm Optimization Algorithm for AQI Prediction]</ref>
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# The radial basis function consists of a center position and a width parameter.<ref name="ref_9a81cd99" />
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# is Euclidean norm usually taking 2-norm. is the radial basis function.<ref name="ref_9a81cd99" />
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# Based on the EDIW-PSO algorithm, we optimize the centers, widths, and connection weights of radial basis function (RBF) neural network.<ref name="ref_9a81cd99" />
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# This paper proposed a radial basis function (RBF) neural network method to forecast the wind power generation of WECS.<ref name="ref_a7574031">[https://www.scientific.net/paper-keyword/radial-basis-function-neural-network-rbfnn Radial Basis Function Neural Network (RBFNN)]</ref>
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# 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.<ref name="ref_a7574031" />
 +
# A major kind of neural network, i.e. radial basis function neural network (RBFNN), is used to model the fault diagnosis structure.<ref name="ref_a7574031" />
 +
# A new algorithm for training radial basis function neural network (RBFNN) is presented in this paper.<ref name="ref_a7574031" />
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# The developed path loss prediction models are the radial basis function neural network (RBFNN) and the multilayer perception neural network (MLPNN).<ref name="ref_e58471a9">[https://onlinelibrary.wiley.com/doi/abs/10.1002/dac.4680 Radial basis function neural network path loss prediction model for LTE networks in multitransmitter signal propagation environments]</ref>
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# We present a radial basis function solver for convolutional neural networks that can be directly applied to both distance metric learning and classification problems.<ref name="ref_73850ceb">[https://openreview.net/forum?id=SkFEGHx0Z Nearest Neighbour Radial Basis Function Solvers for Deep Neural...]</ref>
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# 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.<ref name="ref_73850ceb" />
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# Having a radial basis function centred on each training feature is made scalable by treating it as an approximate nearest neighbour search problem.<ref name="ref_73850ceb" />
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# We show that our radial basis function solver outperforms state-of-the-art embedding approaches on the Stanford Cars196 and CUB-200-2011 datasets.<ref name="ref_73850ceb" />
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# 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.<ref name="ref_a2248a35">[https://www.mitpressjournals.org/doi/pdf/10.1162/neco.1991.3.4.579 Improving the Generalization Properties of Radial Basis Function Neural Networks]</ref>
 
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  <references />

2020년 12월 23일 (수) 01:48 판

노트

위키데이터

말뭉치

  1. A Radial basis function is a function whose value depends only on the distance from the origin.[1]
  2. A Radial basis function works by defining itself by the distance from its origin or center.[1]
  3. The Gaussian variation of the Radial Basis Function, often applied in Radial Basis Function Networks, is a popular alternative.[1]
  4. Error estimates for matrix-valued radial basis function interpolation Journal of Approximation Theory 137: 234-249.[2]
  5. Sobolev bounds on functions with scattered zeros, with applications to radial basis function surface fitting Mathematics of Computation 74: 643-763.[2]
  6. Local error estimates for radial basis function interpolation of scattered data IMA Journal of Numerical Analysis 13: 13-27.[2]
  7. To avoid this problem, the radial basis function interpolation approach can be applied.[3]
  8. For example, suppose the radial basis function is simply the distance from each location, so it forms an inverted cone over each location.[4]
  9. If you take a cross section of the x,z plane for y = 5, you will see a slice of each radial basis function.[4]
  10. In this research, an optimal Radial Basis Function (RBF) neural network-enhanced adaptive robust Kalman filter (KF) method is proposed to isolate and mitigate the influence of the two types of errors.[5]
  11. The radial basis function, based on the radius, r, given by the norm (default is Euclidean distance); the default is ‘multiquadric’: 'multiquadric' : sqrt (( r / self .[6]
  12. Radial basis function methods are modern ways to approximate multivariate functions, especially in the absence of grid data.[7]
  13. AR-RBFN utilizes radial basis function networks (RBFN) initially proposed to perform accurate interpolation of data points in a multidimensional space21.[8]
  14. Figure 2 Attractor ranked radial basis function network.[8]
  15. Learning radial basis function network requires the determination of RBF weights and centers.[8]
  16. (10), the type of the radial basis function \({\psi }_{\rho }({M}_{l}^{g})\)is taken as Gaussian kernels whose inputs are \(E\)-dimensional vectors of a combination of variables and time-lags.[8]
  17. To get a better characterization of freeform surface, Gaussian radial basis function (RBF) model was first proposed and applied in the design of the head-worn display (HWD) systems by Cakmakci et al.[9]
  18. (166) To find a compactly supported radial basis function, the coefficients for both the numerator and the denominator must be found to ensure the compactness of the support.[10]
  19. RBF, radial basis function As can be observed, the inverse functions perform as well as both Wendland or Wu functions, and the rational functions also perform similarly.[10]
  20. The toolbox is called the Matlab Radial Basis Function Toolbox (MRBFT).[11]
  21. The result of a radial basis function applied to two vectors is a single value where smaller values indicate the two vectors are farther apart.[12]
  22. {http://proceedings.mlr.press/v51/que16.html}, abstract = {Radial Basis Function (RBF) networks are a classical family of algorithms for supervised learning.[13]
  23. %V 51 %W PMLR %X Radial Basis Function (RBF) networks are a classical family of algorithms for supervised learning.[13]
  24. The task mentioned above — magically separating points with one line — is known as the radial basis function kernel, with applications in the powerful Support Vector Machine (SVM) algorithm.[14]
  25. We present a radial basis function solver for convolutional neural networks that can be directly applied to both distance metric learning and classification problems.[15]
  26. 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.[15]
  27. Having a radial basis function centred on each training feature is made scalable by treating it as an approximate nearest neighbour search problem.[15]
  28. We show that our radial basis function solver outperforms state-of-the-art embedding approaches on the Stanford Cars196 and CUB-200-2011 datasets.[15]
  29. The radial basis function (RBF) surrogate model represents the interpolating function as a linear combination of basis functions, one for each training point.[16]
  30. A radial basis function (RBF) network is a software system that can classify data and make predictions.[17]
  31. This article assumes you have advanced programming skills with C# and a basic familiarity with the radial basis function network input-process-output mechanism.[17]
  32. Using the distance value by itself is called a linear radial basis function or linear RBF.[18]

소스

  1. ↑ 1.0 1.1 1.2 Radial Basis Functions
  2. ↑ 2.0 2.1 2.2 Radial basis function
  3. ↑ A New Approach to Radial Basis Function Approximation and Its Application to QSAR
  4. ↑ 4.0 4.1 How radial basis functions work—ArcGIS Pro
  5. ↑ An Optimal Radial Basis Function Neural Network Enhanced Adaptive Robust Kalman Filter for GNSS/INS Integrated Systems in Complex Urban Areas
  6. ↑ scipy.interpolate.Rbf — SciPy v1.5.4 Reference Guide
  7. ↑ Radial basis functions
  8. ↑ 8.0 8.1 8.2 8.3 Attractor Ranked Radial Basis Function Network: A Nonparametric Forecasting Approach for Chaotic Dynamic Systems
  9. ↑ Model of radial basis functions based on surface slope for optical freeform surfaces
  10. ↑ 10.0 10.1 Two new classes of compactly supported radial basis functions for approximation of discrete and continuous data
  11. ↑ The Matlab Radial Basis Function Toolbox
  12. ↑ How to Create a Radial Basis Function Network Using C# -- Visual Studio Magazine
  13. ↑ 13.0 13.1 Back to the Future: Radial Basis Function Networks Revisited
  14. ↑ Radial Basis Functions, RBF Kernels, & RBF Networks Explained Simply
  15. ↑ 15.0 15.1 15.2 15.3 Nearest Neighbour Radial Basis Function Solvers for Deep Neural...
  16. ↑ Radial basis functions — SMT 0.7.1 documentation
  17. ↑ 17.0 17.1 Test Run - Radial Basis Function Network Training
  18. ↑ Regularized Linear Regression with Radial Basis Functions

노트

위키데이터

말뭉치

  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]

소스

  1. ↑ Radial Basis Function Networks - an overview
  2. ↑ [ Archived Post Radial Basis Function Artificial Neural Networks]
  3. ↑ 3.0 3.1 Software Analysis Files and Solutions
  4. ↑ 4.0 4.1 Radial basis function neural networks: A topical state-of-the-art survey
  5. ↑ Radial Basis Function Neural Networks (with parameter selection using K-means)
  6. ↑ 6.0 6.1 6.2 Radial basis function network
  7. ↑ 7.0 7.1 7.2 Radial Basis Functions
  8. ↑ 8.0 8.1 8.2 Radial basis function
  9. ↑ 9.0 9.1 Using Neural Networks for Regression: Radial Basis Function Networks
  10. ↑ 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. ↑ 11.0 11.1 11.2 11.3 Radial Basis Function Neural Network (RBFNN)
  12. ↑ Radial basis function neural network path loss prediction model for LTE networks in multitransmitter signal propagation environments
  13. ↑ 13.0 13.1 13.2 13.3 Nearest Neighbour Radial Basis Function Solvers for Deep Neural...
  14. ↑ Improving the Generalization Properties of Radial Basis Function Neural Networks
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