The Adaptive Ensemble of OP-ELM Using Forward-Backward Selection

2013 ◽  
Vol 427-429 ◽  
pp. 1666-1669
Author(s):  
Bo Han ◽  
Bo He ◽  
Meng Meng Ma
Keyword(s):  
Neural Network ◽  
Extreme Learning Machine ◽  
Selection Algorithm ◽  
New Approach ◽  
Adaptive Weights ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Learning Machine

Extreme learning machine (ELM) as a neural network algorithm has shown its good performance in regression or classification applications, but it has a weak robustness. In this paper, a new approach called The Adaptive Ensemble Of OP-ELM using Forward-Backward Selection (AEOP-ELM) is presented, it consists of two significant steps, firstly, we use forward-Backward selection algorithm to select the inputs which will ensure the robustness of the output, then, we train several independent OP-ELM models, and we test them iteratively to find the adaptive weights which will improve the accuracy of the output. The experiments indicate the AEOP-ELM achieves a better robustness than the original ELM as well as a better accuracy.

A New Approach to Machinery Monitoring and Diagnostics Using Self-Organizing Maps

1995 ◽  
Author(s):  
Siyu Zhang ◽  
R. Ganesan ◽  
T. S. Sankar
Keyword(s):  
Neural Network ◽  
Service Life ◽  
Trend Analysis ◽  
New Approach ◽  
Machine System ◽  
Network Algorithm ◽  
On Line ◽  
Neural Network Algorithm ◽  
Monitoring And Diagnostics ◽  
Self Organizing

Abstract The problem of estimating an unknown multivariate function from on-line vibration measurements, for determining the conditions of a machine system and for estimating its service life is considered. This problem is formulated into a multiple-index based trend analysis problem and the corresponding indices for trend analysis are extracted from the on-line vibration data. Selection of these indices is based on the simultaneous consideration of commonly-observed faults or malfunctions in the machine system being monitored. A neural network algorithm that has been developed by the present authors for multiple-index based regression is adapted to perform the trend analysis of a machine system. Applications of this neural network algorithm to the condition monitoring and life estimation of both a bearing system as well as a gearbox are fully demonstrated. The efficiency and computational supremacy of the new algorithm are established through comparing with the performance of Self-Organizing Mapping (SOM) and Constrained Topological Mapping (CTM) algorithms. Further, the usefulness of multiple-index based trend analysis in precisely predicting the condition and service life of a machine system is clearly demonstrated. Using on-line vibration signal to constitute the set of variables for trend analysis, and employing the newly-developed self-organizing neural algorithm for performing the trend analysis, a new approach is developed for machinery monitoring and diagnostics.

A New Approach to Analyzing Gas Mixtures Based on Improved BP Neural Network Algorithm

2012 ◽  
Vol 260-261 ◽  
pp. 548-553
Author(s):  
Teng Li ◽  
Xiao Mei Yuan ◽  
Shi Liang Yang ◽  
Xin Hui Zhang
Keyword(s):  
Neural Network ◽  
Numerical Simulation ◽  
Bp Neural Network ◽  
Array Signal Processing ◽  
Sensor Arrays ◽  
Gas Mixtures ◽  
New Approach ◽  
Gas Concentration ◽  
Network Algorithm ◽  
Neural Network Algorithm

A new approach is presented for analyzing gas mixtures by transforming the problem into a pattern classification one to reduce the effect of the poor repeatability of sensor response on the prediction of gas concentration. The aim of numerical simulation is to determine how successfully the approach using the combination of artificial neural networks with multi-sensor arrays can analyze multi-component gas mixtures. The results indicate that the new approach is realistic for gas mixture analysis, and numerical simulation is a powerful tool to determine the architecture of a network. By constructing improved BP neural network algorithm and basic BP neural network into sensor array signal processing and extracting 6 component as the input of neural network, Our investigation results indicated that recognition results obtained from improved BP neural network algorithm more accuracy than the results obtained from basic BP neural network.

scholarly journals Deep Extreme Learning Machine and Its Application in EEG Classification

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Shifei Ding ◽  
Nan Zhang ◽  
Xinzheng Xu ◽  
Lili Guo ◽  
Jian Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Deep Learning ◽  
Extreme Learning Machine ◽  
Learning Algorithm ◽  
Eeg Classification ◽  
Neural Network Algorithm ◽  
Artificial Neural ◽  
Learning Machine

Recently, deep learning has aroused wide interest in machine learning fields. Deep learning is a multilayer perceptron artificial neural network algorithm. Deep learning has the advantage of approximating the complicated function and alleviating the optimization difficulty associated with deep models. Multilayer extreme learning machine (MLELM) is a learning algorithm of an artificial neural network which takes advantages of deep learning and extreme learning machine. Not only does MLELM approximate the complicated function but it also does not need to iterate during the training process. We combining with MLELM and extreme learning machine with kernel (KELM) put forward deep extreme learning machine (DELM) and apply it to EEG classification in this paper. This paper focuses on the application of DELM in the classification of the visual feedback experiment, using MATLAB and the second brain-computer interface (BCI) competition datasets. By simulating and analyzing the results of the experiments, effectiveness of the application of DELM in EEG classification is confirmed.

scholarly journals APPLICATION OF BACK PROPAGATION NEURAL NETWORK IN SPORTS FATIGUE INDICATORS

2021 ◽  
Vol 27 (3) ◽  
pp. 249-252
Author(s):  
Xiaoli Wang ◽  
Chunmin Dai
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Principal Component ◽  
Level Of Evidence ◽  
Bp Network ◽  
Training Time ◽  
Emg Signal ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Learning Machine

ABSTRACT Introduction High-intensity rehabilitation training will produce exercise fatigue. Objective A backpropagation (BP) network neural algorithm is proposed to predict sports fatigue based on electromyography (EMG) signal images. Methods The principal component analysis algorithm is used to reduce the dimension of EMG signal features. The knee joint angle is estimated by the regularized over-limit learning machine algorithm and the BP neural network algorithm. Results The RMSE value of the regularized over-limit learning machine algorithm is lower than that of the BP neural network algorithm. At the same time, the ρ value of the regularized over-limit learning machine algorithm is closer to 1, indicating its higher accuracy. Conclusions The model training time of the regularized over-limit learning machine algorithm has been greatly reduced, which improves efficiency. Level of evidence II; Therapeutic studies - investigation of treatment results.

A NEURAL NETWORK ALGORITHM FOR DENSITY MEASUREMENT OF MULTIPHASE FLOW

2012 ◽  
Vol 24 (2) ◽  
pp. 89-103 ◽  
Author(s):  
Nabeel Al-Rawahi ◽  
Mahmoud Meribout ◽  
Ahmed Al-Naamany ◽  
Ali Al-Bimani ◽  
Adel Meribout
Keyword(s):  
Neural Network ◽  
Multiphase Flow ◽  
Density Measurement ◽  
Network Algorithm ◽  
Neural Network Algorithm
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