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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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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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Application of RBF Neural Network in WEDM
Advanced Materials Research, 2012It is difficult to build a strict mathematical model for WEDM due to the complication of the machining process and the nonlinear relation between process parameters and process targets. The neural network is suited to the modeling of complex system, because it has the functions of self-organized, self-learning and associative memory, and properties of ...
Shi Ping Zhang +3 more
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Analytic fuzzy RBF neural network
1998 Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.98TH8353), 2002An analytic fuzzy neural network with a modified RBF architecture and fuzzy weights is introduced. The fuzzy weights are non-symmetric fuzzy numbers. The learning algorithm is based on a gradient technique.
A. Kandel +2 more
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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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RBF Neural Networks and Descartes’ Rule of Signs
2002We establish versions of Descartes' rule of signs for radial basis function (RBF) neural networks. These RBF rules of signs provide tight bounds for the number of zeros of univariate networks with certain parameter restrictions. Moreover, they can be used to derive tight bounds for the Vapnik-Chervonenkis (VC) dimension and pseudo-dimension of these ...
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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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An efficient multilayer RBF neural network and its application to regression problems
Neural Computing and Applications, 2021Qinghua Jiang, Lailai Zhu, C Shu
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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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