A fast learning method for feedforward neural networks

Wang, Shitong; Chung, Fu-Lai; Jun, Wang; Wu, Jun

doi:10.1016/j.neucom.2014.01.065

Cited by 18 publications

(17 citation statements)

References 28 publications

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“…Thus, according to the above theoretical analysis, we may realize a LA between the output layer and the last hidden layer, with randomly assigned kernel parameter vectors in every hidden layer. When we understand the behavior of multi-layer FKNN networks in the above way, we can see the same benefits [14,28] as LLM, which is summarized as follows:…”

Section: Remarkmentioning

confidence: 99%

“…KLDA = KPCA + LA. In [14,25,26], Wang et al proved that kernelized SVM and its variants are equivalent to KPCA + SVM or SVM's variants. In the same year, Zhang in [23] gave more general results.…”

Section: Remarkmentioning

confidence: 99%

“…For example, except for the most frequently used MSE error function, we can also choose other error functions including these in Table 1. As we may know well, as two universal approximators with probability one [14], ELM (extreme learning machine) and its general version LLM (least learning machine) are actually based on the MSE error function to realize its fast training of a single hidden layer feedforward neural network through simply learning Remark 4. We can get the generalized LLM by replacing the loss function used by LLM with other loss functions.…”

Section: Remarkmentioning

confidence: 99%

“…That is to say, by the generalized LLM we mean that it is a family of regressors which uses LLM's framework with other loss function instead of MSE loss function only. When a single hidden layer FKNN network is applied to function approximation problems, as stated in [14,28], if we take ridge regression as a LA in its last layer, we can obtain the least learning machine (LLM) for training on the given training dataset:…”

Section: Remarkmentioning

confidence: 99%

See 3 more Smart Citations

Feedforward kernel neural networks, generalized least learning machine, and its deep learning with application to image classification

Wang

Jiang

Chung

et al. 2015

Applied Soft Computing

Self Cite

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Section: Remarkmentioning

confidence: 99%

Section: Remarkmentioning

confidence: 99%

Section: Remarkmentioning

confidence: 99%

Section: Remarkmentioning

confidence: 99%

See 2 more Smart Citations

Feedforward kernel neural networks, generalized least learning machine, and its deep learning with application to image classification

Wang

Jiang

Chung

et al. 2015

Applied Soft Computing

Self Cite

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“…In comparison with the brain, the function of ANNs is extremely simplified; however, their capacity to solve various nonlinear problems is almost known to the researchers working on the modeling of systems with complex nonlinear behavior. Multilayer feed-forward neural networks (FNNs) are a kind of ANN that possess the potentiality to solve complex nonlinear functions and therefore are one of the popular modeling techniques [24][25][26][27][28][29][30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network-based modeling of brain response to flicker light

et al. 2015

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Not only does the modeling of dynamical systems, for instance the biological systems, play an important role in the accurate perception and analysis of these systems, but it also makes the prediction and control of their behavior straightforward. The results of multiple researches in the field of the modeling of biological systems have indicated that the chaotic behavior is a prevalent feature of most complex interactive biological systems. Our results demonstrate that the artificial neural network provides us an effective means to model the underlying dynamics of these systems. In this paper, at first, we represent the results of the use of a multilayer feed-forward neural network to model some famous chaotic systems. The specified neural network is trained with the return maps extracted from the time series. We proceed with the paper by evaluating the accuracy and robustness of our model. The ability of the select neural network to model the dynamics of chosen chaotic systems is verified, even in the presence of noise. Afterwards, we model the brain response to the flicker light. It is known that the brain response to some stimuli such as the flicker light recorded as electroretinogram is an exemplar of chaotic behavior. The need remains, however, for realistic modeling of this behavior of the brain. In this paper, we represent the results of the modeling of this chaotic response by utilizing the proposed neural network. The capability of the neural network to model this specific brain response is confirmed.

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Fast property prediction in an industrial rubber mixing process with local ELM model

Jin

Liu

Gao

2017

J of Applied Polymer Sci

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Online property prediction in industrial rubber mixing processes is not an easy task. An efficient data-driven prediction model is developed in this work. The regularized extreme learning machine (RELM) is utilized as the fundamental soft sensor model. To better capture distinguished characteristics in multiple recipes and operating modes, a just-in-time RELM modeling method is developed. The number of hidden neurons and the value of regularization parameter of the just-in-time RELM model can be efficiently selected using a fast leave-one-out strategy. Consequently, without the time-consuming laboratory analysis process, the Mooney viscosity can be online predicted once a mixing batch has been discharged. The industrial Mooney viscosity prediction results show its better prediction performance in comparison with traditional approaches.

show abstract

A fast learning method for feedforward neural networks

Cited by 18 publications

References 28 publications

Feedforward kernel neural networks, generalized least learning machine, and its deep learning with application to image classification

Feedforward kernel neural networks, generalized least learning machine, and its deep learning with application to image classification

Artificial neural network-based modeling of brain response to flicker light

Fast property prediction in an industrial rubber mixing process with local ELM model

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