Landslide displacement prediction using discrete wavelet transform and extreme learning machine based on chaos theory

2016 ◽  
Vol 75 (20) ◽  
Author(s):  
Faming Huang ◽  
Kunlong Yin ◽  
Guirong Zhang ◽  
Lei Gui ◽  
Beibei Yang ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Extreme Learning Machine ◽  
Chaos Theory ◽  
Discrete Wavelet ◽  
Displacement Prediction ◽  
Landslide Displacement Prediction ◽  
Learning Machine

scholarly journals A Novel Fault Diagnosis Method for Motor Bearing Based on DTCWT and AFSO-KELM

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Yan Lu ◽  
Peijiang Li
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Extreme Learning Machine ◽  
Discrete Wavelet ◽  
Complex Wavelet Transform ◽  
Swarm Optimization ◽  
Artificial Fish Swarm ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine ◽  
Motor Bearing

Aiming at the defects of wavelet transform-based feature extraction and extreme learning machine-based classification, a novel fault diagnosis method for motor bearing, based on dual tree complex wavelet transform and artificial fish swarm optimization-kernel extreme learning machine (DTCWT-AFSO-KELM), is proposed in this paper. Firstly, the dual tree complex wavelet transform instead of the discrete wavelet transform is used to decompose the motor bearing signal; then, the features with large differentiation of motor-bearing fault are extracted; finally, the states of motor bearing are classified by using artificial fish swarm optimization-kernel extreme learning machine. In order to better prove the superiority of this method, four kinds of state data of motor bearing under the conditions of 0 HP (horsepower) load, 1 HP load, 2 HP load, and 3 HP load are used to test. The experimental results indicate that the diagnosis accuracies of DTCWT-AFSO-KELM are obviously better than those of discrete wavelet transform and artificial fish swarm optimization-kernel extreme learning machine (DWT-AFSO-KELM) or discrete wavelet transform and extreme learning machine (DWT-ELM) under different loads.

scholarly journals A Hybrid Method Based on Extreme Learning Machine and Wavelet Transform Denoising for Stock Prediction

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 440
Author(s):  
Dingming Wu ◽  
Xiaolong Wang ◽  
Shaocong Wu
Keyword(s):  
Wavelet Transform ◽  
Extreme Learning Machine ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Discrete Wavelet ◽  
Training Algorithms ◽  
Short Term ◽  
Trend Prediction ◽  
Main Challenge ◽  
Learning Machine

The trend prediction of the stock is a main challenge. Accidental factors often lead to short-term sharp fluctuations in stock markets, deviating from the original normal trend. The short-term fluctuation of stock price has high noise, which is not conducive to the prediction of stock trends. Therefore, we used discrete wavelet transform (DWT)-based denoising to denoise stock data. Denoising the stock data assisted us to eliminate the influences of short-term random events on the continuous trend of the stock. The denoised data showed more stable trend characteristics and smoothness. Extreme learning machine (ELM) is one of the effective training algorithms for fully connected single-hidden-layer feedforward neural networks (SLFNs), which possesses the advantages of fast convergence, unique results, and it does not converge to a local minimum. Therefore, this paper proposed a combination of ELM- and DWT-based denoising to predict the trend of stocks. The proposed method was used to predict the trend of 400 stocks in China. The prediction results of the proposed method are a good proof of the efficacy of DWT-based denoising for stock trends, and showed an excellent performance compared to 12 machine learning algorithms (e.g., recurrent neural network (RNN) and long short-term memory (LSTM)).

Landslide displacement prediction based on multivariate chaotic model and extreme learning machine

2017 ◽  
Vol 218 ◽  
pp. 173-186 ◽  
Author(s):  
Faming Huang ◽  
Jinsong Huang ◽  
Shuihua Jiang ◽  
Chuangbing Zhou
Keyword(s):  
Extreme Learning Machine ◽  
Displacement Prediction ◽  
Landslide Displacement Prediction ◽  
Chaotic Model ◽  
Learning Machine
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