A sequential learning algorithm for self-adaptive resource allocation network classifier

Sundaram, Suresh; Dong, Keming; Kim, H. J.

doi:10.1016/j.neucom.2010.07.003

Cited by 127 publications

(78 citation statements)

References 16 publications

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“…Static neural network with fixed structure cannot represent time-varying dynamics efficiently, so there is a practical need of variable network whose structure and parameters are both adjusted in real time. Construction of variable-structure neural network by sequential learning is a research focus in recent years (Suresh et al, 2010). Sequential learning algorithm is a popular method for representing time-varying system dynamics by variable neural network (Platt, 1991;Liang et al, 2006).…”

Section: Variable Rbf Neural Networkmentioning

confidence: 99%

A hybrid real-time tidal prediction mechanism based on harmonic method and variable structure neural network

Yin

Wang

2015

Engineering Applications of Artificial Intelligence

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keywords:Tidal prediction Hybrid model Variable neural network Harmonic method Sliding data window a b s t r a c t Accurate real time tidal prediction is essential for human activities in coastal and marine fields. Tidal changes are influenced not only by periodic revolutions of celestial bodies but also by time-varying meteorological factors. For accurate real-time tidal prediction, a hybrid prediction mechanism is constructed by taking both advantages of harmonic analysis and neural network. In the proposed mechanism, conventional harmonic analysis is employed for representing the influences of celestial factors; and neural network is used for representing the nonlinear influences of meteorological factors. Furthermore, to represent time-varying tidal dynamics influenced by meteorological factors, a variable neural network is real-time constructed with the neurons and the connecting parameters are adaptively adjusted based on a sliding data window (SDW). The hybrid prediction method uses only the latest short-period data to generate predictions sequentially. Hourly tidal data measured at four American tidal stations are used to validate the effectiveness of the hybrid sequential tidal prediction model. Simulation results of tidal prediction demonstrate that the proposed model can generate accurate short-term prediction of tidal levels at very low computational cost.

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Section: Variable Rbf Neural Networkmentioning

confidence: 99%

A hybrid real-time tidal prediction mechanism based on harmonic method and variable structure neural network

Yin

Wang

2015

Engineering Applications of Artificial Intelligence

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“…In [19], an error correction (ErrCor) algorithm is used for function approximation; this algorithm can achieve a desired error rate with fewer RBF units. Other methods have also been established to identify a proper architecture while maintaining a desired accuracy [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

An incremental learning algorithm for the hybrid RBF-BP network classifier

Wen

Xie

Pei

et al. 2016

EURASIP J. Adv. Signal Process.

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This paper presents an incremental learning algorithm for the hybrid RBF-BP (ILRBF-BP) network classifier. A potential function is introduced to the training sample space in space mapping stage, and an incremental learning method for the construction of RBF hidden neurons is proposed. The proposed method can incrementally generate RBF hidden neurons and effectively estimate the center and number of RBF hidden neurons by determining the density of different regions in the training sample space. A hybrid RBF-BP network architecture is designed to train the output weights. The output of the original RBF hidden layer is processed and connected with a multilayer perceptron (MLP) network; then, a back propagation (BP) algorithm is used to update the MLP weights. The RBF hidden neurons are used for nonlinear kernel mapping and the BP network is then used for nonlinear classification, which improves classification performance further. The ILRBF-BP algorithm is compared with other algorithms in artificial data sets and UCI data sets, and the experiments demonstrate the superiority of the proposed algorithm.

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“…In 2010, Suresh et al has developed a sequential learning algorithm for self-adaptive resource allocation network classifier [4]. The algorithm utilizes self-adaptive error based control parameters to alter the training data sequence, evolve the network architecture, and learn the network parameters.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Threshold Selection for Sequential Learning in Radial Basis Function Networks

Lim¹,

Yeoh²

2016

IJCCE

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For sequential learning in Radial Basis Function (RBF) Networks, there is a requirement for dynamic selection of threshold because a constant threshold is inadequate to accommodate functions of varying amplitudes. In this paper, a new criterion is defined for the dynamic selection of the Euclidean output deviation threshold. Its effect on the learning process experienced by RBF networks with regard to functions of variable amplitude is shown. This improved network can automatically select a suitable threshold for its own supervised learning depending on the objective parameters set to achieve certain accuracy level of the desired output. This paper also proposes further automation to neuron growing and pruning within Radial Basis Function (RBF) neural networks. The proposed dynamic threshold selection technique has shown significant improvement in achieving stable neuron growth rate in dealing with signal amplitude variation.

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A sequential learning algorithm for self-adaptive resource allocation network classifier

Cited by 127 publications

References 16 publications

A hybrid real-time tidal prediction mechanism based on harmonic method and variable structure neural network

A hybrid real-time tidal prediction mechanism based on harmonic method and variable structure neural network

An incremental learning algorithm for the hybrid RBF-BP network classifier

Dynamic Threshold Selection for Sequential Learning in Radial Basis Function Networks

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