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An ART2/RBF Hybrid Neural Networks Research
2005The radial basis function (RBF) neural networks have been widely used for approximation and learning due to its structural simplicity. However, there exist two difficulties in using traditional RBF networks: How to select the optimal number of intermediate layer nodes and centers of these nodes?
Xuhua Yang 0001 +4 more
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A Hybrid Forward Algorithm for RBF Neural Network Construction
IEEE Transactions on Neural Networks, 2006This paper proposes a novel hybrid forward algorithm (HFA) for the construction of radial basis function (RBF) neural networks with tunable nodes. The main objective is to efficiently and effectively produce a parsimonious RBF neural network that generalizes well.
Peng, Jian Xun, Li, Kang, Huang, D.S.
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Simultaneous Optimization of Weights and Structure of an RBF Neural Network
2006We propose here a new evolutionary algorithm, the RBF-Gene algorithm, to optimize Radial Basis Function Neural Networks. Unlike other works on this subject, our algorithm can evolve both the structure and the numerical parameters of the network: it is able to evolve the number of neurons and their weights.
Lefort, Vincent +3 more
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A Growing Algorithm for RBF Neural Network
2009This 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
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On the implementation of RBF technique in neural networks
Proceedings of the conference on Analysis of neural network applications, 1991Mohamad T. Musavi +3 more
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Adaptive RBF Neural Network Control
2012Since the idea of the computational abilities of networks composed of simple models of neurons was introduced in the 1940s [1], neural network techniques have undergone great developments and have been successfully applied in many fields such as learning, pattern recognition, signal processing, modeling, and system control.
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An efficient multilayer RBF neural network and its application to regression problems
Neural Computing and Applications, 2021Qinghua Jiang +2 more
exaly
Discrete RBF Neural Network Control
2017The discrete-time implementation of controllers is important. There are two methods for designing the digital controller. One method, called emulation, is to design a controller based on the continuous-time system, then discrete the controller.
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RBF Neural Network Design and Simulation
2012This 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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