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.

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 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 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 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 A hybrid deep kernel incremental extreme learning machine based on improved coyote and beetle swarm optimization methods

2021 ◽  
Author(s):  
Di Wu ◽  
Ting Li ◽  
Qin Wan
Keyword(s):  
Extreme Learning Machine ◽  
Optimization Algorithm ◽  
Optimization Methods ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Intelligent Optimization ◽  
Intelligent Optimization Algorithm ◽  
Mutation Strategies ◽  
Learning Machine ◽  
Hidden Layer

AbstractThe iteration times and learning efficiency of kernel incremental extreme learning machines are always affected by the redundant nodes. A hybrid deep kernel incremental extreme learning machine (DKIELM) based on the improved coyote and beetle swarm optimization methods was proposed in this paper. A hybrid intelligent optimization algorithm based on the improved coyote optimization algorithm (ICOA) and improved beetle swarm optimization algorithm (IBSOA) was proposed to optimize the parameters and determine the number of effectively hidden layer neurons for the proposed DKIELM. A Gaussian global best-growing operator was adopted to replace the original growing operator in the intelligent optimization algorithm to improve COA searching efficiency and convergence. In the meantime, IBSOA was designed based on tent mapping inverse learning and dynamic mutation strategies to avoid falling into a local optimum. The experimental results demonstrated the feasibility and effectiveness of the proposed DKIELM with encouraging performances compared with other ELMs.

scholarly journals Improve Efficiency of Extreme Learning Machine based on Chaos Particle Swarm Optimization Method

2019 ◽  
Vol 8 (03) ◽  
pp. 24491-24501
Author(s):  
Yuwen Pan Zhan Wen ◽  
Yahui Chen, Wenzao Li
Keyword(s):  
Particle Swarm Optimization ◽  
Extreme Learning Machine ◽  
High Speed ◽  
Particle Swarm ◽  
Optimization Method ◽  
Running Speed ◽  
Swarm Optimization ◽  
Fast Training ◽  
Learning Machine ◽  
Hidden Layer

Extreme Learning Machine (ELM) and Regularized Extreme Learning Machine (RELM) have advantages of fast training speed and good generalization. However, ELM/RELM often needs numerous number of hidden layer nodes to get better performance. The superabundant nodes in hidden layer maybe lead to low running speed. Thus it is not feasible to use ELM in some fields that require high speed algorithms. Therefore, in this paper, we propose an Improved ELM/RELM Optimized based on Chaos Particle Swarm Optimization (CPSO-ELM/RELM) to reduce the number of hidden layer nodes, but still maintain a desirable accuracy. At the same time, it lowers the running speed compared with other algorithms. To verify the application of this method, we design numerous experiments for ELM and RRELM. Their simulation shows that the approach improves the speed of the algorithms, and the accuracy is still high. This makes it possible to use improved CPSO-ELM/RELM in some system with high real-time requirements.

Negative Correlation Hidden Layer for the Extreme Learning Machine

2021 ◽  
pp. 107482
Author(s):  
Carlos Perales-González ◽  
Francisco Fernández-Navarro ◽  
Javier Pérez-Rodríguez ◽  
Mariano Carbonero-Ruz
Keyword(s):  
Extreme Learning Machine ◽  
Negative Correlation ◽  
Learning Machine ◽  
Hidden Layer
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