Results 221 to 230 of about 59,339 (255)

A Generalized Growing and Pruning RBF (GGAP-RBF) Neural Network for Function Approximation

IEEE Transactions on Neural Networks, 2005
This paper presents a new sequential learning algorithm for radial basis function (RBF) networks referred to as generalized growing and pruning algorithm for RBF (GGAP-RBF). The paper first introduces the concept of significance for the hidden neurons and then uses it in the learning algorithm to realize parsimonious networks.
Guang-Bin, Huang, P, Saratchandran, Narasimhan, Sundararajan   +2 more
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RBF Neural Network Case Teaching Research

2011
In this paper, the RBF neural network case teaching has been studied. In the actual teaching process, we find it more difficult for student to learn the course, duing to the RBF neural network curriculum theory is more stronger. Many students do not know how to use the theory to solve practical problems.Therefore, we equip students with basic knowledge
JingBing Li, HuaiQiang Zhang, YouLing Zhou, Yong Bai   +3 more
openaire   +1 more source

RBF Neural Network Design and Simulation

2012
This chapter introduces RBF neural network design method, gives RBF neural network approximation algorithm based on gradient descent, analyzes the effects of Gaussian function parameters on RBF approximation, and introduces RBF neural network modeling method based on off-line training. Several simulation examples are given.
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Intrusion Detection Based on RBF Neural Network

2009 International Symposium on Information Engineering and Electronic Commerce, 2009
Radial Basis Function (RBF) has been one of the most common neural networks used in the intrusion detection system(IDS). To improve the approximation performance and calculation speed of RBF, we describe a method to deal with the benchmark datasets adopted in the research. It includes converting the string to numeric elements firstly, then omitting the
Jing Bi, Kun Zhang, Xiaojing Cheng
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Time series forecasting with RBF neural network

2005 International Conference on Machine Learning and Cybernetics, 2005
Radial basis function neural network (RBF NN) has been widely used for nonlinear system identification because of its simple topological structure and its ability to reveal how learning proceeds in an explicit manner. In this paper, descriptions and original applications of RBF NN, to the time series forecasting problem is presented.
null Xiang-Bin Yan, null Zhen Wang, null Shu-Hua Yu, null Yi-Jun Li   +3 more
openaire   +1 more source

Adaptive RBF neural networks for pattern classifications

Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290), 2003
The viewpoints are presented that the centers and widths of kernels in RBF networks should be determined by a self-learning procedure, that a new kernel naturally comes into being according to which class some labeled patterns are misclassified to, and going a step further, that a current kernel be deleted if its effect on the test set is too trivial ...
null Gao Daqi, null Yang Genxing
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RBF, SOM, Hopfield, and Deep Neural Networks

2020
In this chapter, four different types of neural networks are described: Radial Basis Functions-RBF, Self-Organizing Maps-SOM, the Hopfield, and the deep neural networks. RBF uses a different approach in the design of a neural network based on the hidden layer (unique in the network) composed of neurons in which radial basis functions are defined, hence
Arcangelo Distante, Cosimo Distante
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Coking flue temperature RBF neural network model

The 27th Chinese Control and Decision Conference (2015 CCDC), 2015
A modified radial basis function neural networks (RBFNN) model is proposed to solve the control problem that the flue temperature in coke oven usually has the properties of high nonlinearity, large time-delay and multiple disturbances. The proposed method adopts K-means to initialize hidden layer and center parameters of the network.
Zhang Li, Xu Qingyang, Jin Shibo, Li Jiangning   +3 more
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Robust Image Watermarking Using RBF Neural Network

2006
In recent years digital watermarking was developed significantly and applied broadly for copyright protection and authentication. In this paper, a digital image watermarking scheme is developed using neural network to embedded watermark into DCT domain of each subimage blocks obtained by subsampling, which achieves adaptively watermark embedding and ...
Wei Lu, Hongtao Lu, Fu-Lai Chung
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A Growing Algorithm for RBF Neural Network

2009
This paper presents a growing algorithm to design the architecture of RBF neural network called growing RBF neural network algorithm (GRBF). The GRBF starts from a single prototype randomly initialized in the feature space; the whole algorithm consists of two major parts: the structure learning phase and parameter adjusting phase.
Han Honggui, Qiao Junfei
openaire   +1 more source