scholarly journals Aero Engine Fault Diagnosis Using an Optimized Extreme Learning Machine

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Xinyi Yang ◽  
Shan Pang ◽  
Wei Shen ◽  
Xuesen Lin ◽  
Keyi Jiang ◽  
...  
Keyword(s):  
Gas Turbine ◽  
Extreme Learning Machine ◽  
Validation Data ◽  
Data Set ◽  
Swarm Optimization ◽  
Turbine Engine ◽  
Optimal Network ◽  
Aero Engine ◽  
Learning Machine ◽  
Hidden Layer

A new extreme learning machine optimized by quantum-behaved particle swarm optimization (QPSO) is developed in this paper. It uses QPSO to select optimal network parameters including the number of hidden layer neurons according to both the root mean square error on validation data set and the norm of output weights. The proposed Q-ELM was applied to real-world classification applications and a gas turbine fan engine diagnostic problem and was compared with two other optimized ELM methods and original ELM, SVM, and BP method. Results show that the proposed Q-ELM is a more reliable and suitable method than conventional neural network and other ELM methods for the defect diagnosis of the gas turbine engine.

scholarly journals Aero Engine Component Fault Diagnosis Using Multi-Hidden-Layer Extreme Learning Machine with Optimized Structure

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Shan Pang ◽  
Xinyi Yang ◽  
Xiaofeng Zhang
Keyword(s):  
Fault Diagnosis ◽  
Gas Turbine ◽  
Extreme Learning Machine ◽  
Network Structure ◽  
Gas Turbine Engine ◽  
Data Set ◽  
Turbine Engine ◽  
Engine Component ◽  
Learning Machine ◽  
Hidden Layer

A new aero gas turbine engine gas path component fault diagnosis method based on multi-hidden-layer extreme learning machine with optimized structure (OM-ELM) was proposed. OM-ELM employs quantum-behaved particle swarm optimization to automatically obtain the optimal network structure according to both the root mean square error on training data set and the norm of output weights. The proposed method is applied to handwritten recognition data set and a gas turbine engine diagnostic application and is compared with basic ELM, multi-hidden-layer ELM, and two state-of-the-art deep learning algorithms: deep belief network and the stacked denoising autoencoder. Results show that, with optimized network structure, OM-ELM obtains better test accuracy in both applications and is more robust to sensor noise. Meanwhile it controls the model complexity and needs far less hidden nodes than multi-hidden-layer ELM, thus saving computer memory and making it more efficient to implement. All these advantages make our method an effective and reliable tool for engine component fault diagnosis tool.

OMP-ELM: Orthogonal Matching Pursuit-Based Extreme Learning Machine for Regression

2015 ◽  
Vol 24 (1) ◽  
pp. 135-143 ◽  
Author(s):  
Omer F. Alcin ◽  
Abdulkadir Sengur ◽  
Jiang Qian ◽  
Melih C. Ince
Keyword(s):  
Extreme Learning Machine ◽  
Matching Pursuit ◽  
Computational Cost ◽  
Machine Intelligence ◽  
Orthogonal Matching Pursuit ◽  
Training Data ◽  
Singularity Problem ◽  
Data Set ◽  
Learning Machine ◽  
Hidden Layer

AbstractExtreme learning machine (ELM) is a recent scheme for single hidden layer feed forward networks (SLFNs). It has attracted much interest in the machine intelligence and pattern recognition fields with numerous real-world applications. The ELM structure has several advantages, such as its adaptability to various problems with a rapid learning rate and low computational cost. However, it has shortcomings in the following aspects. First, it suffers from the irrelevant variables in the input data set. Second, choosing the optimal number of neurons in the hidden layer is not well defined. In case the hidden nodes are greater than the training data, the ELM may encounter the singularity problem, and its solution may become unstable. To overcome these limitations, several methods have been proposed within the regularization framework. In this article, we considered a greedy method for sparse approximation of the output weight vector of the ELM network. More specifically, the orthogonal matching pursuit (OMP) algorithm is embedded to the ELM. This new technique is named OMP-ELM. OMP-ELM has several advantages over regularized ELM methods, such as lower complexity and immunity to the singularity problem. Experimental works on nine commonly used regression problems indicate that the investigated OMP-ELM method confirms these advantages. Moreover, OMP-ELM is compared with the ELM method, the regularized ELM scheme, and artificial neural networks.

scholarly journals Local Coupled Extreme Learning Machine Based on Particle Swarm Optimization

Algorithms ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 174 ◽  
Author(s):  
Hongli Guo ◽  
Bin Li ◽  
Wei Li ◽  
Fengjuan Qiao ◽  
Xuewen Rong ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Extreme Learning Machine ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Network Size ◽  
Benchmark Problems ◽  
Swarm Optimization ◽  
Learning Machine ◽  
Hidden Layer ◽  
Configuration Simulation

We developed a new method of intelligent optimum strategy for a local coupled extreme learning machine (LC-ELM). In this method, both the weights and biases between the input layer and the hidden layer, as well as the addresses and radiuses in the local coupled parameters, are determined and optimized based on the particle swarm optimization (PSO) algorithm. Compared with extreme learning machine (ELM), LC-ELM and extreme learning machine based on particle optimization (PSO-ELM) that have the same network size or compact network configuration, simulation results in terms of regression and classification benchmark problems show that the proposed algorithm, which is called LC-PSO-ELM, has improved generalization performance and robustness.

scholarly journals Data Fusion Algorithm for Heterogeneous Wireless Sensor Networks Based on Extreme Learning Machine Optimized by Particle Swarm Optimization

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Li Cao ◽  
Yong Cai ◽  
Yinggao Yue
Keyword(s):  
Particle Swarm Optimization ◽  
Data Fusion ◽  
Extreme Learning Machine ◽  
Particle Swarm ◽  
Sensor Nodes ◽  
Redundant Information ◽  
Swarm Optimization ◽  
Heterogeneous Wireless Sensor Networks ◽  
Learning Machine ◽  
Hidden Layer

Data fusion can reduce the data communication time between sensor nodes, reduce energy consumption, and prolong the lifetime of the network, making it an important research focus in the field of heterogeneous wireless sensor networks (HWSNs). Normal sensor nodes are susceptible to external environmental interferences, which affect the measurement results. In addition, raw data contain redundant information. The transmission of redundant information consumes excess energy, thereby reducing the lifetime of the network. We propose a data fusion method based on an extreme learning machine optimized by particle swarm optimization for HWSNs. The spatiotemporal correlation between the data of the HWSNs is determined, and the extreme learning machine method is used to process the data collected by the sensor nodes in the hierarchical routing structure of the HWSN. The particle swarm optimization algorithm is used to optimize the input weight matrix and the hidden layer bias of the extreme learning machine. An output weight matrix is created to reduce the number of hidden layer nodes and improve the generalization ability of the model. The data fusion model fuses the original data collected by the sensor nodes. The simulation results show that the proposed algorithm reduces network energy consumption and improves the lifetime of the network, the efficiency of data fusion, and the reliability of data transmission compared with other data fusion methods.

scholarly journals Incremental Multiple Hidden Layers Regularized Extreme Learning Machine Based on Forced Positive-Definite Cholesky Factorization

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Jingyi Liu ◽  
Ba Tuan Le
Keyword(s):  
Prediction Model ◽  
Extreme Learning Machine ◽  
Network Structure ◽  
Prediction Accuracy ◽  
Computational Cost ◽  
Positive Definite ◽  
Cholesky Factorization ◽  
Optimal Network ◽  
Learning Machine ◽  
Hidden Layer

The theory and implementation of extreme learning machine (ELM) prove that it is a simple, efficient, and accurate machine learning method. Compared with other single hidden layer feedforward neural network algorithms, ELM is characterized by simpler parameter selection rules, faster convergence speed, and less human intervention. The multiple hidden layer regularized extreme learning machine (MRELM) inherits these advantages of ELM and has higher prediction accuracy. In the MRELM model, the number of hidden layers is randomly initiated and fixed, and there is no iterative tuning process. However, the optimal number of hidden layers is the key factor to determine the generalization ability of MRELM. Given this situation, it is obviously unreasonable to determine this number by trial and random initialization. In this paper, an incremental MRELM training algorithm (FC-IMRELM) based on forced positive-definite Cholesky factorization is put forward to solve the network structure design problem of MRELM. First, an MRELM-based prediction model with one hidden layer is constructed, and then a new hidden layer is added to the prediction model in each training step until the generalization performance of the prediction model reaches its peak value. Thus, the optimal network structure of the prediction model is determined. In the training procedure, forced positive-definite Cholesky factorization is used to calculate the output weights of MRELM, which avoids the calculation of the inverse matrix and Moore-Penrose generalized inverse of matrix involved in the training process of hidden layer parameters. Therefore, FC-IMRELM prediction model can effectively reduce the computational cost brought by the process of increasing the number of hidden layers. Experiments on classification and regression problems indicate that the algorithm can be effectively used to determine the optimal network structure of MRELM, and the prediction model training by the algorithm has excellent performance in prediction accuracy and computational cost.

scholarly journals Medical Dataset Classification: A Machine Learning Paradigm Integrating Particle Swarm Optimization with Extreme Learning Machine Classifier

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
C. V. Subbulakshmi ◽  
S. N. Deepa
Keyword(s):  
Machine Learning ◽  
Particle Swarm Optimization ◽  
Extreme Learning Machine ◽  
Particle Swarm ◽  
Learning Paradigm ◽  
Swarm Optimization ◽  
Generalization Performance ◽  
Learning Machine ◽  
Hidden Layer ◽  
Medical Dataset

Medical data classification is a prime data mining problem being discussed about for a decade that has attracted several researchers around the world. Most classifiers are designed so as to learn from the data itself using a training process, because complete expert knowledge to determine classifier parameters is impracticable. This paper proposes a hybrid methodology based on machine learning paradigm. This paradigm integrates the successful exploration mechanism called self-regulated learning capability of the particle swarm optimization (PSO) algorithm with the extreme learning machine (ELM) classifier. As a recent off-line learning method, ELM is a single-hidden layer feedforward neural network (FFNN), proved to be an excellent classifier with large number of hidden layer neurons. In this research, PSO is used to determine the optimum set of parameters for the ELM, thus reducing the number of hidden layer neurons, and it further improves the network generalization performance. The proposed method is experimented on five benchmarked datasets of the UCI Machine Learning Repository for handling medical dataset classification. Simulation results show that the proposed approach is able to achieve good generalization performance, compared to the results of other classifiers.

scholarly journals Research of Pose Control Algorithm of Coal Mine Rescue Snake Robot

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Yun Bai ◽  
YuanBin Hou
Keyword(s):  
Extreme Learning Machine ◽  
Coal Mine ◽  
Control Algorithm ◽  
Weight Coefficient ◽  
Swarm Optimization ◽  
Snake Robot ◽  
Pose Control ◽  
Learning Machine ◽  
Hidden Layer ◽  
Mine Rescue

Aiming at how to achieve optimal control of joint pitch angles in the process of the robot surmounting obstacle, taking the developed coal mine rescue snake robot as an experimental platform, a pose control algorithm based on particle swarm optimization weight coefficient of extreme learning machine (PSOELM) is proposed. In order to obtain the optimized hidden layer matrix of the extreme learning machine (ELM), particle swarm optimization (PSO) is applied to optimize the weight coefficient of hidden layer matrix. The simulation and experiment results prove that, compared with the ELM algorithm, the smaller mean square error (MSE) between the joint pitch angles of robot and the expected values is acquired by the PSOELM, which overcomes the shortcoming that traditional extreme learning machine cannot reach the best performance because of the random selection of the parameters of the hidden layer nodes. PSOELM is superior to ELM algorithm in control accuracy, fast searching for the optimal and stability. Optimal control of robot’s joint pitch angles is achieved. The algorithm is applied to the surmounting obstacle control of the developed snake robot, and it lays the foundation for further implement of the coal mine rescue.

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