Training radial basis function networks using biogeography-based optimizer

Aljarah, Ibrahim; Faris, Hossam; Mirjalili, Seyedali; Al-Madi, Nailah

doi:10.1007/s00521-016-2559-2

Cited by 89 publications

(38 citation statements)

References 46 publications

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“…Recently, many new meta-heuristic algorithms have been used for learning such biogeography-based optimizer (BBO) [64], Moth-flame optimization [91], multi-verse optimizer (MVO) [27], Grey Wolf optimizer (GWO) [63], and many others [8,9,26,28,29,38].…”

Section: Related Workmentioning

confidence: 99%

Evolving neural networks using bird swarm algorithm for data classification and regression applications

et al. 2019

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This work proposes a new evolutionary multilayer perceptron neural networks using the recently proposed Bird Swarm Algorithm. The problem of finding the optimal connection weights and neuron biases is first formulated as a minimization problem with mean square error as the objective function. The BSA is then used to estimate the global optimum for this problem. A comprehensive comparative study is conducted using 13 classification datasets, three function approximation datasets, and one real-world case study (Tennessee Eastman chemical reactor problem) to benchmark the performance of the proposed evolutionary neural network. The results are compared with well-regarded conventional and evolutionary trainers and show that the proposed method provides very competitive results. The paper also considers a deep analysis of the results, revealing the flexibility, robustness, and reliability of the proposed trainer when applied to different datasets.

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Section: Related Workmentioning

confidence: 99%

Evolving neural networks using bird swarm algorithm for data classification and regression applications

et al. 2019

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“…Here, these 10 subfigures correspond to 10 different validation sets in the cross-validation. Furthermore, we compared the proposed PSO-BL method with six traditional machine learning methods to identify the geographical origin of coal samples, including SVM, K-Nearest Neighbour (K-NN) [36], Radial Basis Function Neural Network (RBFNN) [37], the BPNN algorithm, RF and the BL model. Principal components analysis was employed to reduce the dimension and hence to enhance the computational efficiency of the SVM model.…”

Section: Model Constructionmentioning

confidence: 99%

Identification of Coal Geographical Origin Using Near Infrared Sensor Based on Broad Learning

Lei

Rao

et al. 2019

Applied Sciences

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Geographical origin, an important indicator of the chemical composition and quality grading, is one essential factor that should be taken into account in evaluating coal quality. However, traditional coal origin identification methods based on chemistry experiments are not only time consuming and labour intensive, but also costly. Near-Infrared (NIR) spectroscopy is an effective and efficient way to measure the chemical compositions of samples and has demonstrated excellent performance in various fields of quantitative and qualitative research. In this study, we employ NIR spectroscopy to identify coal origin. Considering the fact that the NIR spectra of coal samples always contain a large amount of redundant information and the number of samples is small, the broad learning algorithm is utilized here as the modelling system to classify the coal geographical origin. In addition, the particle swarm optimization algorithm is introduced to improve the structure of the Broad Learning (BL) model. We compare the improved model with the other five multivariate classification methods on a dataset with 243 coal samples collected from five countries. The experimental results indicate that the improved BL model can achieve the highest overall accuracy of 97.05%. The results obtained in this study suggest that the NIR technique combined with machine learning methods has significant potential for further development of coal geographical origin identification systems.

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“…Various Meta-Heuristic algorithms were explored to solve different types of problems such as global function optimization [20], optimizing neural networks [21][22][23][24], software effort estimation [25], and parameter estimation problem for manufacturing processes. Due to space constraints, we focus only on closely related work of based estimation problem for manufacturing processes that used nature-inspired algorithms.…”

Section: Nature-inspired Metaheuristicsmentioning

confidence: 99%

Estimating ARMA Model Parameters of an Industrial Process Using Meta-Heuristic Search Algorithms

Sheta

Faris²,

Aljarah³

2018

IJET

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This paper addresses the parameter estimation problem for a manufacturing process based on the Auto-Regressive Moving Average (ARMA) model. The accurate estimation of the ARMA model’s parameter helps to reduce the production costs, provide better product quality, increase productivity and profit. Meta-heuristic algorithms are among these approximate techniques which have been successfully used to search for an optimal solution in complex search space. Meta-heuristic algorithms can converge to an optimal global solution despite traditional parameter estimation techniques which stuck by local optimal. A comparison between Meta-heuristic algorithms: Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and the Accelerated PSO, Cuckoo Search, Krill Herd and Firefly algorithm is provided to handle the parameter estimation problem for a Winding process in the industry. The developed ARMA-meta-heuristics models for a winding machine are evaluated based on different evaluation metrics. The results reveal that meta-heuristics can provide an outstanding modeling performance.

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Training radial basis function networks using biogeography-based optimizer

Cited by 89 publications

References 46 publications

Evolving neural networks using bird swarm algorithm for data classification and regression applications

Evolving neural networks using bird swarm algorithm for data classification and regression applications

Identification of Coal Geographical Origin Using Near Infrared Sensor Based on Broad Learning

Estimating ARMA Model Parameters of an Industrial Process Using Meta-Heuristic Search Algorithms

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