Extreme learning machine and back propagation neural network comparison for temperature and humidity control of oyster mushroom based on microcontroller

2017 ◽  
Author(s):  
G. M. Fuady ◽  
A.H. Turoobi ◽  
M. N. Majdi ◽  
M. Syaiin ◽  
R.Y. Adhitya ◽  
...  
Keyword(s):  
Neural Network ◽  
Extreme Learning Machine ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Oyster Mushroom ◽  
Humidity Control ◽  
Network Comparison ◽  
Temperature And Humidity ◽  
Learning Machine

scholarly journals Improving annual precipitation prediction using extreme learning machine with wavelet packet decomposition

2021 ◽  
Author(s):  
Hua Wang ◽  
Wenchuan Wang ◽  
Yu-jin Du ◽  
Dong-mei Xu ◽  
Yi-duo Zhang
Keyword(s):  
Extreme Learning Machine ◽  
Wavelet Packet ◽  
Moving Average ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Annual Precipitation ◽  
Wavelet Packet Decomposition ◽  
Precipitation Forecasting ◽  
Precipitation Prediction ◽  
Learning Machine

Abstract Accurate precipitation prediction can help plan for different water resources management demands and provide an extension of lead-time for the tactical and strategic planning of courses of action as well as activity. In this paper, a novel merged precipitation prediction framework (ELM-WPD) is proposed on the Extreme learning machine (ELM) with wavelet packet decomposition (WPD). The model can be described as the following: (a) we use the WPD to decompose the original precipitation data into several sub-layers; (b) ELM model is employed to complete the forecasting calculation for the decomposed series; (c) the results are integrated to complete the final prediction. Four quantitative indexes (RMSE, MAE, R and NSEC) are employed for the comparison criteria. The results are compared with Back-propagation neural network (BPNN), autoregressive integrated moving average model (ARIMA), ELM, BPNN-WPD model, ARIMA-WPD, indicating that the ELM-WPD model has better performance than other models used in this paper. Hence, the proposed method can provide higher accuracy and reliability for annual precipitation forecasting and can be extended to similar situations for precipitation forecasting.

scholarly journals Performance Sensing Data Prediction for an Aircraft Auxiliary Power Unit Using the Optimized Extreme Learning Machine

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3935 ◽  
Author(s):  
Xiaolei Liu ◽  
Liansheng Liu ◽  
Lulu Wang ◽  
Qing Guo ◽  
Xiyuan Peng
Keyword(s):  
Neural Network ◽  
Extreme Learning Machine ◽  
Power Unit ◽  
Back Propagation ◽  
Performance Parameter ◽  
Sensing Data ◽  
Auxiliary Power Unit ◽  
Auxiliary Power ◽  
Learning Machine ◽  
Parameter Prediction

The aircraft auxiliary power unit (APU) is responsible for environmental control in the cabin and the main engines starting the aircraft. The prediction of its performance sensing data is significant for condition-based maintenance. As a complex system, its performance sensing data have a typically nonlinear feature. In order to monitor this process, a model with strong nonlinear fitting ability needs to be formulated. A neural network has advantages of solving a nonlinear problem. Compared with the traditional back propagation neural network algorithm, an extreme learning machine (ELM) has features of a faster learning speed and better generalization performance. To enhance the training of the neural network with a back propagation algorithm, an ELM is employed to predict the performance sensing data of the APU in this study. However, the randomly generated weights and thresholds of the ELM often may result in unstable prediction results. To address this problem, a restricted Boltzmann machine (RBM) is utilized to optimize the ELM. In this way, a stable performance parameter prediction model of the APU can be obtained and better performance parameter prediction results can be achieved. The proposed method is evaluated by the real APU sensing data of China Southern Airlines Company Limited Shenyang Maintenance Base. Experimental results show that the optimized ELM with an RBM is more stable and can obtain more accurate prediction results.

scholarly journals A Machine Learning Method for the Detection of Brown Core in the Chinese Pear Variety Huangguan Using a MOS-Based E-Nose

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4499 ◽  
Author(s):  
Hao Wei ◽  
Yu Gu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Learning Algorithms ◽  
Economic Value ◽  
Back Propagation ◽  
Principal Component ◽  
Back Propagation Neural Network ◽  
Machine Learning Algorithms ◽  
Oxide Semiconductor ◽  
Learning Machine

The brown core is an internal disorder that significantly affects the palatability and economic value of Chinese pears. In this study, a framework that includes a back-propagation neural network (BPNN) and extreme learning machine (ELM) (BP-ELMNN) was proposed for the detection of brown core in the Chinese pear variety Huangguan. The odor data of pear were collected using a metal oxide semiconductor (MOS) electronic nose (E-nose). Principal component analysis was used to analyze the complexity of the odor emitted by pears with brown cores. The performances of several machine learning algorithms, i.e., radial basis function neural network (RBFNN), BPNN, and ELM, were compared with that of the BP-ELMNN. The experimental results showed that the proposed framework provided the best results for the test samples, with an accuracy of 0.9683, a macro-precision of 0.9688, a macro-recall of 0.9683, and a macro-F1 score of 0.9685. The results demonstrate that the use of machine learning algorithms for the analysis of E-nose data is a feasible and non-destructive method to detect brown core in pears.

Extreme Learning Machine-Based Traffic Incidents Detection with Domain Adaptation Transfer Learning

2017 ◽  
Vol 26 (4) ◽  
pp. 601-612
Author(s):  
Chaimae Elhatri ◽  
Mohammed Tahifa ◽  
Jaouad Boumhidi
Keyword(s):  
Extreme Learning Machine ◽  
Transfer Learning ◽  
Domain Adaptation ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Incident Detection ◽  
Target Domain ◽  
Traffic Incidents ◽  
Learning Machine ◽  
Past Experiences

AbstractTraffic incidents in big cities are increasing alongside economic growth, causing traffic delays and deteriorating road safety conditions. Thus, developing a universal freeway automatic incident detection (AID) algorithm is a task that took the interest of researchers. This paper presents a novel automatic traffic incident detection method based on the extreme learning machine (ELM) algorithm. Furthermore, transfer learning has recently gained popularity as it can successfully generalise information across multiple tasks. This paper aimed to develop a new approach for the traffic domain-based domain adaptation. The ELM was used as a classifier for detection, and target domain adaptation transfer ELM (TELM-TDA) was used as a tool to transfer knowledge between environments to benefit from past experiences. The detection performance was evaluated by common criteria including detection rate, false alarm rate, and others. To prove the efficiency of the proposed method, a comparison was first made between back-propagation neural network and ELM; then, another comparison was made between ELM and TELM-TDA.

scholarly journals Biomedical Arrhythmia Heart Diseases Classification Based on Artificial Neural Network and Machine Learning Approach

2018 ◽  
Vol 7 (3.27) ◽  
pp. 10 ◽  
Author(s):  
Stalin Subbiah ◽  
Suresh Subramanian
Keyword(s):  
Neural Network ◽  
Heart Diseases ◽  
Back Propagation ◽  
Medical Application ◽  
Back Propagation Neural Network ◽  
Support Vector ◽  
Ecg Signal ◽  
Ecg Signals ◽  
Machine Learning Approach ◽  
Learning Machine

In present day, several types of developments are carried toward the medical application.  There has been increased improvement in the processing of ECG signals. The accurate detection of ECG signals with the help of detection of P, Q, R and S waveform. However these waveforms are suffered from some disturbances like noise.  Initially denoising the ECG signal using filters and detect the PQRS waveforms. Four filters are carried out to remove the ECG noises that are Median, Gaussian, FIR and Butterworth filter. ECG signal is analyzed or classify using Extreme Learning Machine (ELM) and it compared with Support Vector Machine (SVM) and Back Propagation Neural Network (BPNN). The paper classifies the ECG signal into two classes, Normal and Abnormal. ECG waveform is detected and analyzed using the 48 records of the MIT-BIH arrhythmia database. Denoising results are evaluated using MSE, RMSE, PSNR, NAE and NCC. The classifier performance is measured in terms of Sensitivity (Se), Positive Predictivity (PP) and Specificity (SP).   

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