Metaheuristic-based extreme learning machines: a review of design formulations and applications

Eshtay, Mohammed; Faris, Hossam; Obeid, Nadim

doi:10.1007/s13042-018-0833-6

Cited by 46 publications

(12 citation statements)

References 84 publications

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“…When the randomly generated input weight value is zero in ELM, these neurons in the hidden layer are invalidated [51,52], which lead to the decrease in the accuracy of degradation prediction for PEMFC. GA can optimize the input weight and the threshold of hidden layer neurons in ELM to solve this problem and improve the accuracy.…”

Section: Ga-elmmentioning

confidence: 99%

Proton Exchange Membrane Fuel Cell Prognostics Using Genetic Algorithm and Extreme Learning Machine

2020

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The prognostics can predict the degradation of proton exchange membrane fuel cell (PEMFC) to formulate a reasonable maintenance plan for improving its lifetime and performance. In this paper, the voltage degradation for PEMFC at different conditions is predicted, by using a novel prognostics method based on genetic algorithm (GA) and extreme learning machine (ELM). The novel prognostics method considers the effects of the PEMFC load current, relative humidity, hydrogen pressure, and temperature on the degradation for PEMFC. Firstly, the voltage degradation prediction model for PEMFC is built by the ELM. Then, the GA is adopted to determine the optimal parameter of the proposed degradation prediction model. Finally, the voltage degradation prediction of the proposed prognostics method is validated, using data derived from the PEMFC in actual postal fuel cell electric vehicle (PFCEV) at real conditions and PEMFC at dynamic load current. The experimental results show that the proposed prognostics method can obtain good voltage degradation prediction for PEMFC in PFCEV at real conditions. The proposed method achieves better voltage degradation prediction for PEMFC at dynamic load current than other traditional data‐based prognostics methods.

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Section: Ga-elmmentioning

confidence: 99%

Proton Exchange Membrane Fuel Cell Prognostics Using Genetic Algorithm and Extreme Learning Machine

2020

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“…where ( 6) and (7), as shown at the bottom of the page, H is called the hidden layer output matrix of the neural network and the i th column of H is the i th hidden node output with respect to inputs x 1 , x 2 , • • • , x N . The smallest norm least-squares solution of the above linear system is:…”

Section: A Classic Elmmentioning

confidence: 99%

“…Compared with other classifiers, it has significant advantages in training speed and accuracy. As a promising algorithm, it is widely used in various related researches [7], including the design and implementation of CAD system.…”

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confidence: 99%

Computer-Aided Diagnosis Based on Extreme Learning Machine: A Review

et al. 2020

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Computer-Aided Diagnosis (CAD) can improve the accuracy of diagnosis effectively, reduce the rate of misdiagnosis, and provide the support for the valid decision. In clinical applications, high requirements are often imposed on the execution speed and accuracy of CAD systems. The classifier is regarded as the core of the CAD system, that is, the performance of the classifier will have a decisive influence on the operating affection of the CAD system. Extreme Learning Machine (ELM) is a fast learning algorithm using Single Hidden Layer Feedforward Neural Network (SLFN) structure. With its advantages in training speed, generalization performance and accuracy, ELM has draw attention in many research fields, including the development of CAD system. The applications of ELM in CAD are reviewed in this research. First, the mathematical model of ELM and framework of CAD system are briefly introduced. Then, the application of ELM in CAD is reviewed in detail, including the feature modeling method combined with ELM in CAD and the specific application of ELM. Finally, we summarized the current research status of CAD systems based on ELM, and the future work is prospected.INDEX TERMS Computer-aided diagnosis, extreme learning machine, machine learning, review. I. INTRODUCTION

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“…Additionally, choosing the proper "solver algorithm" and defining its best configuration is also a difficult task due to the existence of several solvers characterized by different parametrizations. Metaheuristics are widely recognized as powerful solvers for COPs, even for hard optimization problems [2,[4][5][6][7][8]. In some situations, they are the only feasible approach due to the dimensionality of the search space that characterizes the COP at hand.…”

Section: Introductionmentioning

confidence: 99%

Combinatorial Optimization Problems and Metaheuristics: Review, Challenges, Design, and Development

Peres¹,

Castelli²

2021

Applied Sciences

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In the past few decades, metaheuristics have demonstrated their suitability in addressing complex problems over different domains. This success drives the scientific community towards the definition of new and better-performing heuristics and results in an increased interest in this research field. Nevertheless, new studies have been focused on developing new algorithms without providing consolidation of the existing knowledge. Furthermore, the absence of rigor and formalism to classify, design, and develop combinatorial optimization problems and metaheuristics represents a challenge to the field’s progress. This study discusses the main concepts and challenges in this area and proposes a formalism to classify, design, and code combinatorial optimization problems and metaheuristics. We believe these contributions may support the progress of the field and increase the maturity of metaheuristics as problem solvers analogous to other machine learning algorithms.

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Metaheuristic-based extreme learning machines: a review of design formulations and applications

Cited by 46 publications

References 84 publications

Proton Exchange Membrane Fuel Cell Prognostics Using Genetic Algorithm and Extreme Learning Machine

Proton Exchange Membrane Fuel Cell Prognostics Using Genetic Algorithm and Extreme Learning Machine

Computer-Aided Diagnosis Based on Extreme Learning Machine: A Review

Combinatorial Optimization Problems and Metaheuristics: Review, Challenges, Design, and Development

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