scholarly journals COD Optimization Prediction Model Based on CAWOA-ELM in Water Ecological Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Lili Jiang ◽  
Liu Yang ◽  
Yang Huang ◽  
Yi Wu ◽  
Huixian Li ◽  
...  
Keyword(s):  
Prediction Model ◽  
Search Strategy ◽  
Oxygen Demand ◽  
Ecological Environment ◽  
Model Combining ◽  
Chaos Search ◽  
Whale Optimization ◽  
Poor Convergence ◽  
Learning Machine ◽  
Effective Water

The change of water quality can reflect the important indicators of ecological environment measurement. Sewage discharge is an important factor causing environmental pollution. Establishing an effective water ecological prediction model can detect changes in the ecological environment system quickly and effectively. In order to detect high error rate and poor convergence of the water ecological chemical oxygen demand (COD) prediction model, combining the limit learning machine (ELM) model and whale optimization algorithm, CAWOA is improved by the sin chaos search strategy, while the ELM optimizes the parameters of the algorithm to improve convergence speed, thus improving the generalization performance of the ELM. In the CAWOA, the global optimization results of the WOA are promoted by introducing a sin chaotic search strategy and adaptive inertia weights. On this basis, the COD prediction model of CAWOA-ELM is established and compared with similar algorithms by using the optimized ELM to predict the water ecological COD in a region. Finally, from the experimental results of the CAWOA-ELM algorithm, it has excellent prediction effect and practical application value.

scholarly journals Prediction of Chemical Gas Emissions Based on Ecological Environment

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Guobin Chen ◽  
Shijin Li
Keyword(s):  
Prediction Model ◽  
Search Strategy ◽  
Model Simulation ◽  
Ecological Environment ◽  
Gray Wolf ◽  
Simulation Experiments ◽  
Chaotic Search ◽  
Training Samples ◽  
Reduce Emissions ◽  
Learning Machine

With the serious pollution of the ecological environment, there are a large number of harmful gases in the chemical gases emitted by the industry. Relevant intelligent chemical algorithms control the emission of chemical gases, which can effectively reduce emissions and predict emissions more accurately. This paper proposes a gray wolf optimization algorithm based on chaotic search strategy combined with extreme learning machine to predict chemical emission gases, taking a 330 MW pulverized coal-fired boiler as a test object and establishing chemical emissions of CNGWO-ELM. The prediction model, by using the relevant data collected by DCS as training samples and test samples, trains and tests the model. Simulation experiments show that the chemical emission prediction model of CNGWO-ELM has better accuracy and stronger generalization ability, with higher practical value.

Ultra-short-term wind speed prediction based on variational mode decomposition and optimized extreme learning machine

2021 ◽  
pp. 0309524X2110385
Author(s):  
Lian Lian ◽  
Kan He
Keyword(s):  
Wind Speed ◽  
Prediction Model ◽  
Extreme Learning Machine ◽  
Prediction Accuracy ◽  
Good Prediction ◽  
Variational Mode Decomposition ◽  
Short Term ◽  
Mode Decomposition ◽  
Whale Optimization ◽  
Learning Machine

The main purpose of this paper is to improve the prediction accuracy of ultra-short-term wind speed. It is difficult to predict the ultra-short-term wind speed because of its unstable, non-stationary and non-linear. Aiming at the unstable and non-stationary characteristics of ultra-short-term wind speed, the variational mode decomposition algorithm is introduced to decompose the ultra-short-term wind speed data, and a series of stable and stationary components with different frequencies are obtained. The extreme learning machine with good prediction performance and real-time performance is selected as the prediction model of decomposed components. In order to solve the problem of random setting of input weights and bias of extreme learning machine, whale optimization algorithm is used to optimize extreme learning machine to improve the regression performance. The performance of the developed prediciton model is verified by real ultra-short-term wind speed sample data. Five prediction models are selected as the comparison model. Through the comparison between the predicted value and the actual value, the prediction error and its histogram distribution, eight performance indicators, and Pearson’s test correlation coefficient, the results show that the proposed prediction model has high prediction accuracy.

scholarly journals A Cloud Load Forecasting Model with Nonlinear Changes using Whale Optimization Algorithm Hybrid Strategy - extreme Learning Machine

2021 ◽  
Author(s):  
Hua Peng ◽  
Wu-Shao Wen ◽  
Ming-Lang Tseng ◽  
Ling-Ling Li
Keyword(s):  
Cloud Computing ◽  
Prediction Model ◽  
Extreme Learning Machine ◽  
Load Forecasting ◽  
Forecasting Model ◽  
Random Parameters ◽  
Load Prediction ◽  
Hybrid Strategy ◽  
Whale Optimization ◽  
Learning Machine

Abstract This study proposes a novel cloud load prediction model and combines hybrid whale optimizer (HWOA) and extreme learning machine (ELM) together for strong nonlinear mapping ability. Accurate cloud load prediction improves the cloud service efficiency and serves as the foundation for network scheme due to traditional linear forecasting models are unable to predict cloud computing resources with nonlinear changes on massive multiplication and cloud computing data complexity, effectively. The proposed cloud load forecasting model is to employ HWOA optimizer to optimize the ELM model random parameters. The contributions of this study are as follows. (1) the HWOA optimizer is to solve the whale optimizer local extremum problem; (2) the proposed HWOA optimizer reduces the ELM random parameters on cloud load forecasting; (3) the convergence performance verifies the benchmark testing functions; and (4) three simulation experiments are conducted to test the cloud load forecast effect. The result indicated that the convergence analysis reveals the HWOA optimizer outperforms the prior optimizers. The proposed cloud load prediction model obtains better forecasting results. The mean absolute percentage error and root mean square error of the proposed model are less than 14% and 11, respectively. Accurate cloud load forecasting lays a foundation for effective deployment of cloud computing resources and maximization of economic benefits.

scholarly journals Carbon Price Forecasting Based on Improved CEEMDAN and Extreme Learning Machine Optimized by Sparrow Search Algorithm

2021 ◽  
Vol 13 (9) ◽  
pp. 4896
Author(s):  
Jianguo Zhou ◽  
Dongfeng Chen
Keyword(s):  
Prediction Model ◽  
Extreme Learning Machine ◽  
Emission Reduction ◽  
Search Algorithm ◽  
Carbon Price ◽  
Integration Scheme ◽  
Combination Model ◽  
Price Prediction ◽  
Price Series ◽  
Learning Machine

Effective carbon pricing policies have become an effective tool for many countries to encourage emission reduction. An accurate carbon price prediction model is helpful for the implementation of energy conservation and emission reduction policies and the decision-making of governments and investors. However, it is difficult for a single prediction model to achieve high prediction accuracy because of the high complexity of the carbon price series. Many studies have proved the nonlinear characteristics of carbon trading prices, but there are very few studies on the chaotic nature of carbon price series. As a consequence, this paper proposes an innovative hybrid model for carbon price prediction. A decomposition-reconstruction-prediction-integration scheme is designed to predict carbon prices. Firstly, several intrinsic mode functions (IMFs) and one residue were obtained from the raw data decomposed by ICEEMDAN. Next, the decomposed subsection is reconstructed into a new sequence according to the calculation results by the Lempel-Ziv complexity algorithm. Then, considering the chaotic characteristics of sequence, the input variables of the models are determined through the phase space reconstruction (PSR) algorithm combined with the partial autocorrelation function (PACF). Finally, the Sparrow search algorithm (SSA) is introduced to optimize the extreme learning machine (ELM) model, which is applied in the carbon price prediction for the purpose of verifying the validity of the proposed combination model, which is applied to the pilots of Hubei, Beijing, and Guangdong. The empirical results show that the combination model outperformed the 13 other models in predicting accuracy, speed, and stability. The decomposition-reconstruction-prediction-integration strategy is a method for predicting the carbon price efficiently.

scholarly journals A Carbon Price Prediction Model Based on the Secondary Decomposition Algorithm and Influencing Factors

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1328
Author(s):  
Jianguo Zhou ◽  
Shiguo Wang
Keyword(s):  
Prediction Model ◽  
Extreme Learning Machine ◽  
Influencing Factors ◽  
Search Algorithm ◽  
Carbon Price ◽  
Carbon Trading ◽  
Price Prediction ◽  
Model Based ◽  
Mode Decomposition ◽  
Learning Machine

Carbon emission reduction is now a global issue, and the prediction of carbon trading market prices is an important means of reducing emissions. This paper innovatively proposes a second decomposition carbon price prediction model based on the nuclear extreme learning machine optimized by the Sparrow search algorithm and considers the structural and nonstructural influencing factors in the model. Firstly, empirical mode decomposition (EMD) is used to decompose the carbon price data and variational mode decomposition (VMD) is used to decompose Intrinsic Mode Function 1 (IMF1), and the decomposition of carbon prices is used as part of the input of the prediction model. Then, a maximum correlation minimum redundancy algorithm (mRMR) is used to preprocess the structural and nonstructural factors as another part of the input of the prediction model. After the Sparrow search algorithm (SSA) optimizes the relevant parameters of Extreme Learning Machine with Kernel (KELM), the model is used for prediction. Finally, in the empirical study, this paper selects two typical carbon trading markets in China for analysis. In the Guangdong and Hubei markets, the EMD-VMD-SSA-KELM model is superior to other models. It shows that this model has good robustness and validity.

An Improved Method for CT Image Coding Using PSNR Prediction Model Based on ELM

2015 ◽  
Vol 738-739 ◽  
pp. 598-601
Author(s):  
Han Yang Zhu ◽  
Xin Yu Jin ◽  
Jian Feng Shen
Keyword(s):  
Computational Complexity ◽  
Prediction Model ◽  
Image Coding ◽  
Region Of Interest ◽  
Image Encoding ◽  
Large Size ◽  
Learning Machine ◽  
Intractable Problem ◽  
Encoding Efficiency ◽  
Better Than

In telemedicine, medical images are always considered very important telemedicine diagnostic evidences. High transmission delay in a bandwidth limited network becomes an intractable problem because of its large size. It’s important to achieve a quality balance between Region of Interest (ROI) and Background Region (BR) when ROI-based image encoding is being used. In this paper, a research made on balancing method of LS-SVM based ROI/BR PSNR prediction model to optimize the ROI encoding shows it’s much better than conventional methods but with very high computational complexity. We propose a new method using extreme learning machine (ELM) with lower computational complexity to improve encoding efficiency compared to LS-SVM based model. Besides, it also achieves the same effect of balancing ROI and BR.

scholarly journals Pressure Prediction of Coal Slurry Transportation Pipeline Based on Particle Swarm Optimization Kernel Function Extreme Learning Machine

2015 ◽  
Vol 2015 ◽  
pp. 1-7
Author(s):  
Xue-cun Yang ◽  
Xiao-ru Yan ◽  
Chun-feng Song
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Extreme Learning Machine ◽  
Kernel Function ◽  
Particle Swarm ◽  
Support Vector ◽  
Particle Swarm Algorithm ◽  
Coal Slurry ◽  
Swarm Optimization ◽  
Learning Machine

For coal slurry pipeline blockage prediction problem, through the analysis of actual scene, it is determined that the pressure prediction from each measuring point is the premise of pipeline blockage prediction. Kernel function of support vector machine is introduced into extreme learning machine, the parameters are optimized by particle swarm algorithm, and blockage prediction method based on particle swarm optimization kernel function extreme learning machine (PSOKELM) is put forward. The actual test data from HuangLing coal gangue power plant are used for simulation experiments and compared with support vector machine prediction model optimized by particle swarm algorithm (PSOSVM) and kernel function extreme learning machine prediction model (KELM). The results prove that mean square error (MSE) for the prediction model based on PSOKELM is 0.0038 and the correlation coefficient is 0.9955, which is superior to prediction model based on PSOSVM in speed and accuracy and superior to KELM prediction model in accuracy.

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