Online sequential extreme learning machine with forgetting mechanism

Zhao, Jianwei; Wang, Zhihui; Park, Dong Sun

doi:10.1016/j.neucom.2012.02.003

Cited by 166 publications

(68 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With selecting the appropriate activation function ( ), the TELM calculates (14), so the actual output of the second hidden layer is updated as follows:…”

Section: Two-hidden-layer Elmmentioning

confidence: 99%

“…But ELM only needs to randomly set the weights and bias of the hidden neurons, and the output weights are determined by using the Moore-Penrose pseudoinverse under the criterion of least-squares method. In recent years, various ELM variants have been proposed aiming to achieve better achievements, such as the deep ELM with kernel based on Multilayer Extreme Learning Machine (DELM) algorithm [11]; two-hidden-layer extreme learning machine (TELM) [12]; a Four-Layered Feedforward Neural Network [13]; online sequential extreme learning machine [14,15]; multiple kernel extreme learning machine (MK-ELM) [16]; two-stage extreme learning machine [17], using noise detection and improving the classifier accuracy [18,19].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Multiple Hidden Layers Extreme Learning Machine Method and Its Application

Xiao

Mao

2017

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Extreme learning machine (ELM) is a rapid learning algorithm of the single-hidden-layer feedforward neural network, which randomly initializes the weights between the input layer and the hidden layer and the bias of hidden layer neurons and finally uses the least-squares method to calculate the weights between the hidden layer and the output layer. This paper proposes a multiple hidden layers ELM (MELM for short) which inherits the characteristics of parameters of the first hidden layer. The parameters of the remaining hidden layers are obtained by introducing a method (make the actual output zero error approach the expected hidden layer output). Based on the MELM algorithm, many experiments on regression and classification show that the MELM can achieve the satisfactory results based on average precision and good generalization performance compared to the two-hidden-layer ELM (TELM), the ELM, and some other multilayer ELM.

show abstract

“…With selecting the appropriate activation function ( ), the TELM calculates (14), so the actual output of the second hidden layer is updated as follows:…”

Section: Two-hidden-layer Elmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Multiple Hidden Layers Extreme Learning Machine Method and Its Application

Xiao

Mao

2017

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…In other words, data are only valid within a certain time period. In the process of the FOS-ELM, the data obtained earlier than a certain time can no longer be used under the forgetting mechanism, since the outdated data may make the prediction less accurate [20]. As solar radiation and temperature vary seasonally, the FOS-ELM is more appropriate for the PV power prediction model.…”

Section: Os-elm With Forgetting Mechanism (Fos-elm)mentioning

confidence: 99%

A Short-Term Photovoltaic Power Prediction Model Based on an FOS-ELM Algorithm

2017

View full text Add to dashboard Cite

Abstract:With the increasing proportion of photovoltaic (PV) power in power systems, the problem of its fluctuation and intermittency has become more prominent. To reduce the negative influence of the use of PV power, we propose a short-term PV power prediction model based on the online sequential extreme learning machine with forgetting mechanism (FOS-ELM), which can constantly replace outdated data with new data. We use historical weather data and historical PV power data to predict the PV power in the next period of time. The simulation result shows that this model has the advantages of a short training time and high accuracy. This model can help the power dispatch department schedule generation plans as well as support spatial and temporal compensation and coordinated power control, which is important for the security and stability as well as the optimal operation of power systems.

show abstract

“…From the last chapter we can know,the accuracy of OS-ELM algorithm is related to the initial setting of parameters, and different parameters have different effects on the accuracy of OS-ELM classifier [10] [11]. The parameters of artificial setting are not high, so the parameters are optimized by genetic algorithm.…”

Section: The Transformer Fault Diagnosis Based On Os-elm and Genetic mentioning

confidence: 99%

Optimizing Online Sequential Extreme Learning Machine Parameters and Application to Transformer Fault Diagnosis

Kang¹,

Chen²

2015

Proceedings of the 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering 2015

View full text Add to dashboard Cite

Abstract. In order to solve the problem that the (OS-ELM) is used in the fault diagnosis of the transformer, the genetic algorithm (Algorithm Genetic) is applied to the on-line extreme learning machine, and a new method of transformer fault diagnosis is proposed. In this method, the number of hidden layer neurons of the Block L, the data set size N, and the hidden layer activation function are selected by the Algorithm Genetic optimization algorithm. Through simulation test, the fault diagnosis of transformer is 99.56%, and the test time is 0.0024 s. Compared with the optimization, the diagnostic accuracy and the test time of the transformer fault are improved obviously.

show abstract

Online sequential extreme learning machine with forgetting mechanism

Cited by 166 publications

References 33 publications

A Multiple Hidden Layers Extreme Learning Machine Method and Its Application

A Multiple Hidden Layers Extreme Learning Machine Method and Its Application

A Short-Term Photovoltaic Power Prediction Model Based on an FOS-ELM Algorithm

Optimizing Online Sequential Extreme Learning Machine Parameters and Application to Transformer Fault Diagnosis

Contact Info

Product

Resources

About