An evolutionary extreme learning machine based on chemical reaction optimization

Tran, Thuy Van; Wang, Yao Nan

doi:10.1080/02522667.2016.1220094

Cited by 5 publications

(2 citation statements)

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“…Extreme learning machine (ELM) is a simple learning algorithm for a single hidden layer FNN proposed by Huang et al (2006). In the references Huang et al (2011), Silva et al (2011) and Tran and Wang (2017), it has been demonstrated as a highly effective technique for classification problems. Hence, ELM is chosen for bench marking MHDA training MLPs.…”

Section: Resultsmentioning

confidence: 99%

A study on performance of MHDA in training MLPs

Ranjini

2019

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Purpose In recent years, the application of metaheuristics in training neural network models has gained significance due to the drawbacks of deterministic algorithms. This paper aims to propose the use of a recently developed “memory based hybrid dragonfly algorithm” (MHDA) for training multi-layer perceptron (MLP) model by finding the optimal set of weight and biases. Design/methodology/approach The efficiency of MHDA in training MLPs is evaluated by applying it to classification and approximation benchmark data sets. Performance comparison between MHDA and other training algorithms is carried out and the significance of results is proved by statistical methods. The computational complexity of MHDA trained MLP is estimated. Findings Simulation result shows that MHDA can effectively find the near optimum set of weight and biases at a higher convergence rate when compared to other training algorithms. Originality/value This paper presents MHDA as an alternative optimization algorithm for training MLP. MHDA can effectively optimize set of weight and biases and can be a potential trainer for MLPs.

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Section: Resultsmentioning

confidence: 99%

A study on performance of MHDA in training MLPs

Ranjini

2019

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“…To improve the accuracy of modelling, the distributed extreme learning machines (ELMs) method was presented in [17]. To optimise the parameters of an ELM, optimisation algorithms such as genetic algorithms (GAs), ant colony algorithms, evolutionary strategies, and basic particle swarms were proposed [18][19][20][21], all of which have poor convergence and local extrema.…”

Section: Introductionmentioning

confidence: 99%

Parametric model for microwave filter by using multiple hidden layer output matrix extreme learning machine

Cao

2019

IET Microwaves, Antennas & Propagation

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Smart tuning of a filter depends very much on an accurate parametric model. Here, the authors develop a parametric model for a microwave filter based on an improved extreme learning machine (ELM). First, a coupling matrix is extracted from scattering parameters in undesirable states. The automatic decomposition and modular training strategy greatly reduces the network complexity. Next, by increasing the size of the output matrix of the hidden‐layer, the number of hidden layer nodes can be changed. Finally, the model parameters are optimised by combining the particle swarm optimisation (PSO) algorithm and the differential evolution (DE) algorithm. The presented parametric model benefits from the information of the fusion mechanism with a hybrid optimisation algorithm and succeeds in avoiding slow convergence and premature influence on the system. It also achieves the desired training accuracy within the target time. Compared with prediction based on back‐propagation (BP) neural networks and least‐squares support vector machines (LS‐SVMs), the proposed method can be generalised and has a better modelling accuracy.

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Extreme Learning Machine for Business Sales Forecasts: A Systematic Review

Saldaña-Olivas

Huamán-Tuesta

2020

Proceedings of the 5th Brazilian Technology Symposium

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An evolutionary extreme learning machine based on chemical reaction optimization

Cited by 5 publications

References 15 publications

A study on performance of MHDA in training MLPs

A study on performance of MHDA in training MLPs

Parametric model for microwave filter by using multiple hidden layer output matrix extreme learning machine

Extreme Learning Machine for Business Sales Forecasts: A Systematic Review

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