A Multiple Hidden Layers Extreme Learning Machine Method and Its Application

Xiao, Dong; Li, Beijing; Mao, Yachun

doi:10.1155/2017/4670187

Cited by 58 publications

(38 citation statements)

References 27 publications

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“…These simulations are designed from the aspects of computation complexity (or running time) and accuracy of the CF-OSELM-PRFF by comparison with the FP-ELM, FGR-OSELM, AFGR-OSELM and FOS-MELM [21]. FOS-MELM is an online sequential multiple hidden layers extreme learning machine with forgetting mechanism, which is recently proposed by Xiao et al To make the results of FOS-MELM more stable, a regularization term is introduced into its solving process according to [20].…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…ELM has been successfully applied to many real-world applications, such as retinal vessel segmentation [7], wind speed forecasting [8,9], water network management [10], path-tracking of autonomous mobile robot [11], modelling of drying processes [12], bearing fault diagnosis [13], cybersecurity defense framework [14], crop classification [15], and energy disaggregation [16]. In recent years, ELM has been extended to multilayer ELMs, which play an important role in the deep learning domain [17][18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

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Cholesky Factorization Based Online Sequential Extreme Learning Machines with Persistent Regularization and Forgetting Factor

Zhou

Kui

2019

Symmetry

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The online sequential extreme learning machine with persistent regularization and forgetting factor (OSELM-PRFF) can avoid potential singularities or ill-posed problems of online sequential regularized extreme learning machines with forgetting factors (FR-OSELM), and is particularly suitable for modelling in non-stationary environments. However, existing algorithms for OSELM-PRFF are time-consuming or unstable in certain paradigms or parameters setups. This paper presents a novel algorithm for OSELM-PRFF, named “Cholesky factorization based” OSELM-PRFF (CF-OSELM-PRFF), which recurrently constructs an equation for extreme learning machine and efficiently solves the equation via Cholesky factorization during every cycle. CF-OSELM-PRFF deals with timeliness of samples by forgetting factor, and the regularization term in its cost function works persistently. CF-OSELM-PRFF can learn data one-by-one or chunk-by-chunk with a fixed or varying chunk size. Detailed performance comparisons between CF-OSELM-PRFF and relevant approaches are carried out on several regression problems. The numerical simulation results show that CF-OSELM-PRFF demonstrates higher computational efficiency than its counterparts, and can yield stable predictions.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cholesky Factorization Based Online Sequential Extreme Learning Machines with Persistent Regularization and Forgetting Factor

Zhou

Kui

2019

Symmetry

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“…In general, better performance between SVR and ANN depends upon the available data set and the problem being dealt with. Neural network architecture has hidden layers, which provide the network with its ability to generalize (Xiao, Li, & Mao, 2017). A network with one hidden layer can model any continuous function (Beale & Jackson, 1998).…”

Section: Support Vector Regressionmentioning

confidence: 99%

Artificial neural network‐based modeling of snow properties using field data and hyperspectral imagery

Haq

Ghosh

Rahaman

et al. 2019

Natural Resource Modeling

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This study attempts to model snow wetness and snow density of Himalayan snow cover using a combination of Hyperspectral image processing and Artificial Neural Network (ANN). Initially, a total of 300 spectral signature measurements, synchronized with snow wetness and snow density, were collected in the field. The spectral reflectance of snow was then modeled as a function of snow properties using ANN. Four snow wetness and three snow density models were developed. A strong correlation was observed in near‐infrared and shortwave‐infrared region. The correlation analysis of ANN modeled snow density and snow wetness showed a strong linear relationship with field‐based data values ranging from 0.87–0.90 and 0.88–0.91, respectively. Our results indicate that an Artificial Intelligence (AI) approach, using a combination of Hyperspectral image processing and ANN, can be efficiently used to predict snow properties (wetness and density) in the Himalayan region. Recommendations for resource managers Snow properties, such as snow wetness and snow density are mainly investigated through field‐based survey but rugged terrains, difficult weather conditions, and logistics management issues establish remote sensing as an efficient alternative to monitor snow properties, especially in the mountain environment. Although Hyperspectral remote sensing is a powerful tool to conduct the quantitative analysis of the physical properties of snow, only a few studies have used hyperspectral data for the estimation of snow density and wetness in the Himalayan region. This could be because of the lack of synchronized snow properties data with field‐based spectral acquisitions. In combination with Hyperspectral image processing, Artificial Neural Network (ANN) can be a useful tool for effective snow modeling because of its ability to capture and represent complex input‐output relationships. Further research into understanding the applicability of neural networks to determine snow properties is required to obtain results from large snow cover areas of the Himalayan region.

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“…A Multiple Hidden Layers extreme Learning Machine Method was introduced by Dong Xiao et al [1]. Extreme Method proposes a multi hidden layers which are obtaining the characteristics of parameters from the first hidden layer.…”

Section: Literature Reviewmentioning

confidence: 99%

Determining Hidden Neurons with Variant Experiments in Multilayer Perception using Machine Learning Neural Networks

Sundaram¹,

Karthigai²

2019

IJITEE

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Neural network has broadly been employed in various fields for its efficacy and its superiority. Excellence results provided can be directly provided in various analyses. Besides the variant types of neural network, Multi layer perceptron plays a vital role for its adaptive learning ability. The network makes prediction based on learn of training set. Neural has three layers then the layers are the Input, Hidden and the Output Layers. There may be more than one hidden layer but there is one input and output layer. The hidden or the intermediate layer is considered as an engine of the complete network as it has the non linear activation function and they has a sensational domination in the finishing result . The amount of neurons in three layers determines the excellence of the network. The neuron in the input and the output layer is fixed as per the dataset while for the intermediate layer it is fixed by the user in random. Increase in neuron cause over-fitting while decrease cause under fitting and these assumptions have a great impact in the final outcome. This paper discusses the existing approaches for fixing the hidden neurons and proposes a method to fix the neurons in the intermediate layer and analyse the quality of the group. The proposed procedure has variant approaches to determine the hidden neuron and they are compared. The experiment is done in WEKA and the accuracy is checked with measures.

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A Multiple Hidden Layers Extreme Learning Machine Method and Its Application

Cited by 58 publications

References 27 publications

Cholesky Factorization Based Online Sequential Extreme Learning Machines with Persistent Regularization and Forgetting Factor

Cholesky Factorization Based Online Sequential Extreme Learning Machines with Persistent Regularization and Forgetting Factor

Artificial neural network‐based modeling of snow properties using field data and hyperspectral imagery

Determining Hidden Neurons with Variant Experiments in Multilayer Perception using Machine Learning Neural Networks

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