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Relevant bibliographies by topics / Radial basis function networks (RBFNs)

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Academic literature on the topic 'Radial basis function networks (RBFNs)'

Author: Grafiati

Published: 4 June 2021

Last updated: 4 February 2022

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Journal articles on the topic "Radial basis function networks (RBFNs)"

1

Feng, Hsuan Ming, Ching Chang Wong, and Ji Hwei Horng. "RBFNs Nonlinear Control System Design through BFPSO Algorithm." Applied Mechanics and Materials 764-765 (May 2015): 619–23. http://dx.doi.org/10.4028/www.scientific.net/amm.764-765.619.

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All parameters are automatically extracted by the bacterial foraging particle swarm optimization (BFPSO) algorithm to approach the desired control system. Three parameterize basis function neural networks (RBFNs) model to solve the car-pole system problem. Several free parameters of radial basis functions can be automatically tuned by the direct of the specified fitness function. In additional, the proper number of radial basis functions (RBFs) of the constructed RBFNs can be chosen by the defined fitness function which takes this factor into account. The desired multiple objectives of the RBF

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2

Alqezweeni, Mohie Mortadha, Vladimir Ivanovich Gorbachenko, Maxim Valerievich Zhukov, and Mustafa Sadeq Jaafar. "Efficient Solving of Boundary Value Problems Using Radial Basis Function Networks Learned by Trust Region Method." International Journal of Mathematics and Mathematical Sciences 2018 (June 3, 2018): 1–4. http://dx.doi.org/10.1155/2018/9457578.

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A method using radial basis function networks (RBFNs) to solve boundary value problems of mathematical physics is presented in this paper. The main advantages of mesh-free methods based on RBFN are explained here. To learn RBFNs, the Trust Region Method (TRM) is proposed, which simplifies the process of network structure selection and reduces time expenses to adjust their parameters. Application of the proposed algorithm is illustrated by solving two-dimensional Poisson equation.

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3

Holden, Sean B., and Mahesan Niranjan. "Average-Case Learning Curves for Radial Basis Function Networks." Neural Computation 9, no. 2 (1997): 441–60. http://dx.doi.org/10.1162/neco.1997.9.2.441.

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The application of statistical physics to the study of the learning curves of feedforward connectionist networks has to date been concerned mostly with perceptron-like networks. Recent work has extended the theory to networks such as committee machines and parity machines, and an important direction for current and future research is the extension of this body of theory to further connectionist networks. In this article, we use this formalism to investigate the learning curves of gaussian radial basis function networks (RBFNs) having fixed basis functions. (These networks have also been called

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4

HUANG, DE-SHUANG. "RADIAL BASIS PROBABILISTIC NEURAL NETWORKS: MODEL AND APPLICATION." International Journal of Pattern Recognition and Artificial Intelligence 13, no. 07 (1999): 1083–101. http://dx.doi.org/10.1142/s0218001499000604.

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This paper investigates the capabilities of radial basis function networks (RBFN) and kernel neural networks (KNN), i.e. a specific probabilistic neural networks (PNN), and studies their similarities and differences. In order to avoid the huge amount of hidden units of the KNNs (or PNNs) and reduce the training time for the RBFNs, this paper proposes a new feedforward neural network model referred to as radial basis probabilistic neural network (RBPNN). This new network model inherits the merits of the two old odels to a great extent, and avoids their defects in some ways. Finally, we apply th

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5

Gil Pita, R., R. Vicen, M. Rosa, M. P. Jarabo, P. Vera, and J. Curpian. "Ultrasonic flaw detection using radial basis function networks (RBFNs)." Ultrasonics 42, no. 1-9 (2004): 361–65. http://dx.doi.org/10.1016/j.ultras.2003.11.018.

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6

Joug, Shian Ming, Hsuan Ming Feng, and Dong Hui Guo. "Self-Tuning RBFNs Mobile Robot Systems through Bacterial Foraging Particle Swarm Optimization Learning Algorithm." Applied Mechanics and Materials 284-287 (January 2013): 2128–36. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.2128.

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A radial basis function neural networks (RBFNs) mobile robot control system is automatically developed with the image processing and learned by the bacterial foraging particle swarm optimization (BFPSO) algorithm in this paper. The image-based architecture of robot model is self-generated to travel the routing path in the dynamical and complicated environments. The visible omni-directional image sensors capture the surrounding environment to represent the behavior model of the mobile robot system. Three parameterize RBFNs model with the centers and spreads of each radial basis function, and th

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7

Dash, Ch Sanjeev Kumar, Ajit Kumar Behera, Satchidananda Dehuri, and Sung-Bae Cho. "Radial basis function neural networks: a topical state-of-the-art survey." Open Computer Science 6, no. 1 (2016): 33–63. http://dx.doi.org/10.1515/comp-2016-0005.

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AbstractRadial basis function networks (RBFNs) have gained widespread appeal amongst researchers and have shown good performance in a variety of application domains. They have potential for hybridization and demonstrate some interesting emergent behaviors. This paper aims to offer a compendious and sensible survey on RBF networks. The advantages they offer, such as fast training and global approximation capability with local responses, are attracting many researchers to use them in diversified fields. The overall algorithmic development of RBF networks by giving special focus on their learning

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8

MAYORGA, RENÉ V., and JONATHAN CARRERA. "A RADIAL BASIS FUNCTION NETWORK APPROACH FOR THE COMPUTATION OF INVERSE CONTINUOUS TIME VARIANT FUNCTIONS." International Journal of Neural Systems 17, no. 03 (2007): 149–60. http://dx.doi.org/10.1142/s0129065707001020.

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This Paper presents an efficient approach for the fast computation of inverse continuous time variant functions with the proper use of Radial Basis Function Networks (RBFNs). The approach is based on implementing RBFNs for computing inverse continuous time variant functions via an overall damped least squares solution that includes a novel null space vector for singularities prevention. The singularities avoidance null space vector is derived from developing a sufficiency condition for singularities prevention that conduces to establish some characterizing matrices and an associated performanc

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9

HUANG, DE-SHUANG. "APPLICATION OF GENERALIZED RADIAL BASIS FUNCTION NETWORKS TO RECOGNITION OF RADAR TARGETS." International Journal of Pattern Recognition and Artificial Intelligence 13, no. 06 (1999): 945–62. http://dx.doi.org/10.1142/s0218001499000525.

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This paper extends general radial basis function networks (RBFN) with Gaussian kernel functions to generalized radial basis function networks (GRBFN) with Parzen window functions, and discusses applying the GRBFNs to recognition of radar targets. The equivalence between the RBFN classifiers (RBFNC) with outer-supervised signals of 0 or 1 and the estimate of Parzen windowed probabilistic density is proved. It is pointed out that the I/O functions of the hidden units in the RBFNC can be extended to general Parzen window functions (or called as potential functions). We present using recursive lea

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10

Dash, Ch Sanjeev Kumar, Ajit Kumar Behera, Satchidananda Dehuri, and Sung-Bae Cho. "Differential Evolution-Based Optimization of Kernel Parameters in Radial Basis Function Networks for Classification." International Journal of Applied Evolutionary Computation 4, no. 1 (2013): 56–80. http://dx.doi.org/10.4018/jaec.2013010104.

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In this paper a two phases learning algorithm with a modified kernel for radial basis function neural networks is proposed for classification. In phase one a new meta-heuristic approach differential evolution is used to reveal the parameters of the modified kernel. The second phase focuses on optimization of weights for learning the networks. Further, a predefined set of basis functions is taken for empirical analysis of which basis function is better for which kind of domain. The simulation result shows that the proposed learning mechanism is evidently producing better classification accuracy

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