Preference-driven yield-and-quality optimization for high-sulfur gas sweetening process by extreme learning machine model

2018 ◽  
Vol 22 (S3) ◽  
pp. 6371-6381
Author(s):  
Jianfeng Shang ◽  
Xiaohua Gu ◽  
Liping Yang ◽  
Haihong Tang ◽  
Kun Zhang ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Machine Model ◽  
Quality Optimization ◽  
Yield And Quality ◽  
Gas Sweetening ◽  
Learning Machine

scholarly journals Fault Tolerance Automotive Air-Ratio Control Using Extreme Learning Machine Model Predictive Controller

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Pak Kin Wong ◽  
Hang Cheong Wong ◽  
Chi Man Vong ◽  
Tong Meng Iong ◽  
Ka In Wong ◽  
...  
Keyword(s):  
Fault Tolerance ◽  
Extreme Learning Machine ◽  
External Disturbance ◽  
Feedback Signal ◽  
Ratio Model ◽  
Control Performance ◽  
Machine Model ◽  
Target Values ◽  
Lambda Sensor ◽  
Learning Machine

Effective air-ratio control is desirable to maintain the best engine performance. However, traditional air-ratio control assumes the lambda sensor located at the tail pipe works properly and relies strongly on the air-ratio feedback signal measured by the lambda sensor. When the sensor is warming up during cold start or under failure, the traditional air-ratio control no longer works. To address this issue, this paper utilizes an advanced modelling technique, kernel extreme learning machine (ELM), to build a backup air-ratio model. With the prediction from the model, a limited air-ratio control performance can be maintained even when the lambda sensor does not work. Such strategy is realized as fault tolerance control. In order to verify the effectiveness of the proposed fault tolerance air-ratio control strategy, a model predictive control scheme is constructed based on the kernel ELM backup air-ratio model and implemented on a real engine. Experimental results show that the proposed controller can regulate the air-ratio to specific target values within a satisfactory tolerance under external disturbance and the absence of air-ratio feedback signal from the lambda sensor. This implies that the proposed fault tolerance air-ratio control is a promising scheme to maintain air-ratio control performance when the lambda sensor is under failure or warming up.

Using enhanced crow search algorithm optimization-extreme learning machine model to forecast short-term wind power

2021 ◽  
pp. 115579
Author(s):  
Ling-Ling Li ◽  
Zhi-Feng Liu ◽  
Ming-Lang Tseng ◽  
Korbkul Jantarakolica ◽  
Ming K. Lim
Keyword(s):  
Wind Power ◽  
Extreme Learning Machine ◽  
Search Algorithm ◽  
Algorithm Optimization ◽  
Short Term ◽  
Machine Model ◽  
Learning Machine

scholarly journals Occupancy Prediction Using Differential Evolution Online Sequential Extreme Learning Machine Model

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 4033
Author(s):  
Jonas Bielskus ◽  
Violeta Motuzienė ◽  
Tatjana Vilutienė ◽  
Audrius Indriulionis
Keyword(s):  
Energy Efficiency ◽  
Differential Evolution ◽  
Extreme Learning Machine ◽  
Open Space ◽  
Prediction Models ◽  
Co2 Concentration ◽  
Full Potential ◽  
Machine Model ◽  
Learning Machine ◽  
Occupancy Prediction

Despite increasing energy efficiency requirements, the full potential of energy efficiency is still unlocked; many buildings in the EU tend to consume more energy than predicted. Gathering data and developing models to predict occupants’ behaviour is seen as the next frontier in sustainable design. Measurements in the analysed open-space office showed accordingly 3.5 and 2.7 times lower occupancy compared to the ones given by DesignBuilder’s and EN 16798-1. This proves that proposed occupancy patterns are only suitable for typical open-space offices. The results of the previous studies and proposed occupancy prediction models have limited applications and limited accuracies. In this paper, the hybrid differential evolution online sequential extreme learning machine (DE-OSELM) model was applied for building occupants’ presence prediction in open-space office. The model was not previously applied in this area of research. It was found that prediction using experimentally gained indoor and outdoor parameters for the whole analysed period resulted in a correlation coefficient R2 = 0.72. The best correlation was found with indoor CO2 concentration—R2 = 0.71 for the analysed period. It was concluded that a 4 week measurement period was sufficient for the prediction of the building’s occupancy and that DE-OSELM is a fast and reliable model suitable for this purpose.

scholarly journals Wood Defect Detection Based on Depth Extreme Learning Machine

2020 ◽  
Vol 10 (21) ◽  
pp. 7488
Author(s):  
Yutu Yang ◽  
Xiaolin Zhou ◽  
Ying Liu ◽  
Zhongkang Hu ◽  
Fenglong Ding
Keyword(s):  
Deep Learning ◽  
Extreme Learning Machine ◽  
Defect Detection ◽  
Clustering Algorithm ◽  
Classification Performance ◽  
Image Features ◽  
Test Time ◽  
Machine Model ◽  
Feature Extraction Method ◽  
Learning Machine

The deep learning feature extraction method and extreme learning machine (ELM) classification method are combined to establish a depth extreme learning machine model for wood image defect detection. The convolution neural network (CNN) algorithm alone tends to provide inaccurate defect locations, incomplete defect contour and boundary information, and inaccurate recognition of defect types. The nonsubsampled shearlet transform (NSST) is used here to preprocess the wood images, which reduces the complexity and computation of the image processing. CNN is then applied to manage the deep algorithm design of the wood images. The simple linear iterative clustering algorithm is used to improve the initial model; the obtained image features are used as ELM classification inputs. ELM has faster training speed and stronger generalization ability than other similar neural networks, but the random selection of input weights and thresholds degrades the classification accuracy. A genetic algorithm is used here to optimize the initial parameters of the ELM to stabilize the network classification performance. The depth extreme learning machine can extract high-level abstract information from the data, does not require iterative adjustment of the network weights, has high calculation efficiency, and allows CNN to effectively extract the wood defect contour. The distributed input data feature is automatically expressed in layer form by deep learning pre-training. The wood defect recognition accuracy reached 96.72% in a test time of only 187 ms.

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