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 Short-Term Traffic Flow Forecasting Based on Data-Driven Model

Mathematics ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 152 ◽  
Author(s):  
Su-qi Zhang ◽  
Kuo-Ping Lin
Keyword(s):  
Support Vector Machine ◽  
Traffic Flow ◽  
Extreme Learning Machine ◽  
Support Vector ◽  
Local Optimum ◽  
Short Term ◽  
Swarm Optimization ◽  
Traffic Flow Forecasting ◽  
Learning Machine ◽  
The One

Short-term traffic flow forecasting is the technical basis of the intelligent transportation system (ITS). Higher precision, short-term traffic flow forecasting plays an important role in alleviating road congestion and improving traffic management efficiency. In order to improve the accuracy of short-term traffic flow forecasting, an improved bird swarm optimizer (IBSA) is used to optimize the random parameters of the extreme learning machine (ELM). In addition, the improved bird swarm optimization extreme learning machine (IBSAELM) model is established to predict short-term traffic flow. The main researches in this paper are as follows: (1) The bird swarm optimizer (BSA) is prone to fall into the local optimum, so the distribution mechanism of the BSA optimizer is improved. The first five percent of the particles with better fitness values are selected as producers. The last ten percent of the particles with worse fitness values are selected as beggars. (2) The one-day and two-day traffic flows are predicted by the support vector machine (SVM), particle swarm optimization support vector machine (PSOSVM), bird swarm optimization extreme learning machine (BSAELM) and IBSAELM models, respectively. (3) The prediction results of the models are evaluated. For the one-day traffic flow sequence, the mean absolute percentage error (MAPE) values of the IBSAELM model are smaller than the SVM, PSOSVM and BSAELM models, respectively. The experimental analysis results show that the IBSAELM model proposed in this study can meet the actual engineering requirements.

Overview of Artificial Fish Swarm Algorithm and its Applications in Industrial Problems

2015 ◽  
Vol 815 ◽  
pp. 253-257 ◽  
Author(s):  
Nurezayana Zainal ◽  
Azlan Mohd Zain ◽  
Safian Sharif
Keyword(s):  
Optimization Algorithm ◽  
Social Behaviors ◽  
Global Optimum ◽  
Local Optimum ◽  
Intelligent Optimization ◽  
Artificial Fish Swarm Algorithm ◽  
Individual Fish ◽  
Intelligent Optimization Algorithm ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm

Artificial fish swarm algorithm (AFSA) is a class of swarm intelligent optimization algorithm stimulated by the various social behaviors of fish in search of food. AFSA can search for global optimum through local optimum value search of each individual fish effectively based on simulating of fish-swarm behaviors such as searching, swarming, following and bulletin. This paper presents an overview of AFSA algorithm by describing the evolution of the algorithm along with all the improvements and its combinations with various algorithms and methods as well as its applications in solving industrial problems.

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.

Fabric wrinkle level classification via online sequential extreme learning machine based on improved sine cosine algorithm

2020 ◽  
Vol 90 (17-18) ◽  
pp. 2007-2021 ◽  
Author(s):  
Zhiyu Zhou ◽  
Ruoxi Zhang ◽  
Jianxin Zhang ◽  
Yaming Wang ◽  
Zefei Zhu ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Optimal Parameter ◽  
Classification Model ◽  
Initial Population ◽  
Local Optimum ◽  
Search Spaces ◽  
Proposed Model ◽  
Sine Cosine Algorithm ◽  
Learning Machine ◽  
Hidden Layer

Because it is difficulty to classify level of fabric wrinkle, this paper proposes a fabric winkle level classification model via online sequential extreme learning machine based on improved sine cosine algorithm (SCA). The SCA has excellent global optimization ability, can explore different search spaces, and effectively avoid falling into local optimum. Because the initial population of SCA will have an impact on its optimization speed and quality, the SCA is initialized by differential evolution (DE) to avoid local optimization, and then the output weight and hidden layer bias are optimized; that is, the improved SCA is used to select the optimal parameters of the online sequential extreme learning machine (OSELM) to improve the generalization performance of the algorithm. To verify the performance of the proposed model DE-SCA-OSELM, it will be compared with other algorithms using a fabric wrinkles dataset collected under standard conditions. The experimental results indicate that the proposed model can effectively find the optimal parameter value of OSELM. The average classification accuracy increased by 6.95%, 3.62%, 6.67%, and 3.34%, respectively, compared with the partial algorithms OSELM, SCAELM, RVFL and PSOSVM, which meets expectations.

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