Using Fitness Dependent Optimizer for Training Multi-layer Perceptron,ERICDATA高等教育知識庫
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篇名
Using Fitness Dependent Optimizer for Training Multi-layer Perceptron
並列篇名
Using Fitness Dependent Optimizer for Training Multi-layer Perceptron
作者 Dosti Kh. AbbasTarik A. RashidKarmand H. AbdallaNebojsa BacaninAbeer Alsadoon
英文摘要
This study presents a novel training algorithm depending upon the recently proposed Fitness Dependent Optimizer (FDO). The stability of this algorithm has been verified and performance-proofed in both the exploration and exploitation stages using some standard measurements. This influenced our target to gauge the performance of the algorithm in training multilayer perceptron neural networks (MLP). This study combines FDO with MLP (codename FDO-MLP) for optimizing weights and biases to predict outcomes of students. This study can improve the learning system in terms of the educational background of students besides increasing their achievements. The experimental results of this approach are affirmed by comparing with the Back-Propagation algorithm (BP) and some evolutionary models such as FDO with cascade MLP (FDO-CMLP), Grey Wolf Optimizer (GWO) combined with MLP (GWO-MLP), modified GWO combined with MLP (MGWO-MLP), GWO with cascade MLP (GWO-CMLP), and modified GWO with cascade MLP (MGWO-CMLP). The qualitative and quantitative results prove that the proposed approach using FDO as a trainer can outperform the other approaches using different trainers on the dataset in terms of convergence speed and local optima avoidance. The proposed FDO-MLP approach classifies with a rate of 0.97.
起訖頁 1575-1585
關鍵詞 OptimizationMultilayer perceptronFitness dependent optimizerTraining neural networkAcademic student performance
刊名 網際網路技術學刊  
期數 202112 (22:7期)
出版單位 台灣學術網路管理委員會
DOI 10.53106/160792642021122207011  複製DOI
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