Radial basis function
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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
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위키데이터
- ID : Q1588488