A novel criterion to select hidden neuron numbers in improved back propagation networks for wind speed forecasting

Madhiarasan, M.; Deepa, S. N.

doi:10.1007/s10489-015-0737-z

Cited by 60 publications

(34 citation statements)

References 19 publications

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“…where l is the size of the selected samples, m, i is the measured result of data-point x i , and p, i is the predictive result of data-point [14][15][16].…”

Section: N) (2)mentioning

confidence: 99%

“…Literature [15] exhibited the advantages of using Beta-probability distribution function (pdf) instead of Gaussian pdf for approximating the forecasting error. Based on the above literature [12][13][14][15][16], this work plans to study the error of Beta-distribution between the predicted values x p and the measured values x m in the wind speed forecasting, and pdf of ε…”

Section: Introductionmentioning

confidence: 99%

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Kernel Ridge Regression Model Based on Beta-Noise and Its Application in Short-Term Wind Speed Forecasting

et al. 2019

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The Kernel ridge regression ( K R R) model aims to find the hidden nonlinear structure in raw data. It makes an assumption that the noise in data satisfies the Gaussian model. However, it was pointed out that the noise in wind speed/power forecasting obeys the Beta distribution. The classic regression techniques are not applicable to this case. Hence, we derive the empirical risk loss about the Beta distribution and propose a technique of the kernel ridge regression model based on the Beta-noise ( B N-K R R). The numerical experiments are carried out on real-world data. The results indicate that the proposed technique obtains good performance on short-term wind speed forecasting.

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“…where l is the size of the selected samples, m, i is the measured result of data-point x i , and p, i is the predictive result of data-point [14][15][16].…”

Section: N) (2)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Kernel Ridge Regression Model Based on Beta-Noise and Its Application in Short-Term Wind Speed Forecasting

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Generally speaking, more hidden neurons in the hidden layer will enhance the computational capacity of the ANN, but may cause overfitting, whereas less hidden neurons would cause underfitting. Currently, there are many criterions for setting hidden neurons, mainly based on the numbers of neurons in the input layer and the output layer [29]. However, such criterions are for general purposes and may be not suitable for this paper.…”

Section: Ann For Body Temperature Estimationmentioning

confidence: 99%

Wearable Continuous Body Temperature Measurement Using Multiple Artificial Neural Networks

Song

Zeng

Wang

et al. 2018

IEEE Trans. Ind. Inf.

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Continuous body temperature measurement (CBTM) is of great significance for human health state monitoring. To avoid interfering with users' daily activities, CBTM is usually achieved using wearable noninvasive thermometers. Current wearable noninvasive thermometers employ steady-state models used in nonwearable thermometers; as a result, the reaction time is long and the measurement can be disturbed by users' activities. However, there is no work to solve these issues. In this paper, first, differences between wearable and nonwearable temperature measurement are analyzed. Second, the relationship among the human body temperature, the skin temperature, and the device temperature is modeled based on artificial neural networks (ANNs). Third, this paper proposes a novel multiple ANNsbased wearable CBTM method. Experiments show that the reaction time of the proposed method is about one-tenth of that of other popular wearable noninvasive CBTM methods, while the accuracy and the robustness are improved.Index Terms-Artificial neural network (ANN), continues body temperature measurement (CBTM), noninvasive, wearable computing.

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“…Its generalization ability, however, was not enough for the wind speed prediction performance. In [15], the SVM was modified by a new algorithm to reduce the limitations of the model. However, the wavelet basis still has difficulty to achieve an ideal decomposition in the wind speed sequence [16].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Feedforward Neural Networks Using an Improved Flower Pollination Algorithm for Short-Term Wind Speed Prediction

Ren

Lin

2019

Energies

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It is well known that the inherent instability of wind speed may jeopardize the safety and operation of wind power generation, consequently affecting the power dispatch efficiency in power systems. Therefore, accurate short-term wind speed prediction can provide valuable information to solve the wind power grid connection problem. For this reason, the optimization of feedforward (FF) neural networks using an improved flower pollination algorithm is proposed. First of all, the empirical mode decomposition method is devoted to decompose the wind speed sequence into components of different frequencies for decreasing the volatility of the wind speed sequence. Secondly, a back propagation neural network is integrated with the improved flower pollination algorithm to predict the changing trend of each decomposed component. Finally, the predicted values of each component can get into an overlay combination process and achieve the purpose of accurate prediction of wind speed. Compared with major existing neural network models, the performance tests confirm that the average absolute error using the proposed algorithm can be reduced up to 3.67%.

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A novel criterion to select hidden neuron numbers in improved back propagation networks for wind speed forecasting

Cited by 60 publications

References 19 publications

Kernel Ridge Regression Model Based on Beta-Noise and Its Application in Short-Term Wind Speed Forecasting

Kernel Ridge Regression Model Based on Beta-Noise and Its Application in Short-Term Wind Speed Forecasting

Wearable Continuous Body Temperature Measurement Using Multiple Artificial Neural Networks

Optimization of Feedforward Neural Networks Using an Improved Flower Pollination Algorithm for Short-Term Wind Speed Prediction

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