Neuro-Fuzzy System for 3-DOF Parallel Robot Manipulator

Azar, Ahmad Taher; Aly, Ahmed Magd; Sayed, Abdelrahman Sayed; Radwan, Mahmoud ElBakry; Ammar, Hossam Hassan

doi:10.1109/niles.2019.8909333

Cited by 12 publications

(3 citation statements)

References 18 publications

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“…Algorithms are essential in the fields of machine learning and deep learning for locating specific patterns or recurring structures within data points, particularly in image datasets (El-Shorbagy et al, 2023;Anter et al, 2014Anter et al, , 2015Ashfaq et al, 2022a;Aslam et al, 2021;Azar et al, 2019c;Boulmaiz et al, 2022;Cheema et al, 2020). However, the raw data needs to be thoroughly cleaned to remove any extraneous information before beginning the discovery process.…”

Section: Related Workmentioning

confidence: 99%

Novel Hybrid Genetic Arithmetic Optimization for Feature Selection and Classification of Pulmonary Disease Images

Nivetha,

Hannah Inbarani

2023

International Journal of Sociotechnology and Knowledge Development

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The difficulty in predicting early cancer is due to the lack of early illness indicators. Metaheuristic approaches are a family of algorithms that seek to find the optimal values for uncertain problems with several implications in optimization and classification problems. An automated system for recognizing illnesses can respond with accuracy, efficiency, and speed, helping medical professionals spot abnormalities and lowering death rates. This study proposes the Novel Hybrid GAO (Genetic Arithmetic Optimization algorithm based Feature Selection) (Genetic Arithmetic Optimization Algorithm-based feature selection) method as a way to choose the features for several machine learning algorithms to classify readily available data on COVID-19 and lung cancer. By choosing just important features, feature selection approaches might improve performance. The proposed approach employs a Genetic and Arithmetic Optimization to enhance the outcomes in an optimization approach.

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Section: Related Workmentioning

confidence: 99%

Novel Hybrid Genetic Arithmetic Optimization for Feature Selection and Classification of Pulmonary Disease Images

Nivetha,

Hannah Inbarani

2023

International Journal of Sociotechnology and Knowledge Development

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“…Besides, an adaptive neuro‐fuzzy inference structure model was utilised by Azar et al. [34] to attain the forward kinematics solution of the 3‐DOF planar parallel robot. A basic neural network model proposed by Prado et al.…”

Section: Introductionmentioning

confidence: 99%

“…Meanwhile, Zhang et al [33] reported the kinematic calibration method with an average position error of 0.4 mm for cable-driven parallel robots (CDPR) considering the pulley mechanisms kinematics. Besides, an adaptive neuro-fuzzy inference structure model was utilised by Azar et al [34] to attain the forward kinematics solution of the 3-DOF planar parallel robot. A basic neural network model proposed by Prado et al [30] was used to address the forward kinematics of a wearable 3-RRS (R, S, P, U corresponding to revolute joint, spherical joint, prismatic joint, universal joint, respectively) parallel robot.…”

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confidence: 99%

Kernel extreme learning machine‐based general solution to forward kinematics of parallel robots

Jun-hai

Zhang

2023

CAAI Trans on Intel Tech

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The forward kinematics of parallel robots is a challenging issue due to its highly coupled non‐linear relation among branch chains. This paper presents a novel approach to forward kinematics of parallel robots based on kernel extreme learning machine (KELM). To tackle with the forward kinematics solution of fully parallel robots, the forward kinematics solution of parallel robots is equivalently transformed into a machine learning model first. On this basis, a computational model combining sparrow search algorithm and KELM is then established, which can serve as both regression and classification. Based on SSA‐optimised KELM (SSA‐KELM) established in this study, a binary discriminator for judging the existence of the forward kinematics solution and a multi‐label regression model for predicting the forward kinematics solution are built to obtain the forward kinematics general solution of parallel robots with different structural configurations and parameters. To evaluate the proposed model, a numerical case on this dataset collected by the inverse kinematics model of a typical 6‐DOF parallel robot is conducted, followed by the results manifesting that the binary discriminator with the discriminant accuracy of 88.50% is superior over ELM, KELM, support vector machine and logistic regression. The multi‐label regression model, with the root mean squared error of 0.06 mm for the position and 0.15° for the orientation, outperforms the double‐hidden‐layer back propagation (2‐BP), ELM, KELM and genetic algorithm‐optimised KELM. Furthermore, numerical cases of parallel robots with different structural configurations and parameters are compared with state‐of‐the‐art models. Moreover, these results of numerical simulation and experiment on the host computer demonstrate that the proposed model displays its high precision, high robustness and rapid convergence, which provides a candidate for the forward kinematics of parallel robots.

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Experimental Kinematic Modeling of 6-DOF Serial Manipulator Using Hybrid Deep Learning

Mohamed

Azar

Abbas

et al. 2020

Advances in Intelligent Systems and Computing

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Neuro-Fuzzy System for 3-DOF Parallel Robot Manipulator

Cited by 12 publications

References 18 publications

Novel Hybrid Genetic Arithmetic Optimization for Feature Selection and Classification of Pulmonary Disease Images

Novel Hybrid Genetic Arithmetic Optimization for Feature Selection and Classification of Pulmonary Disease Images

Kernel extreme learning machine‐based general solution to forward kinematics of parallel robots

Experimental Kinematic Modeling of 6-DOF Serial Manipulator Using Hybrid Deep Learning

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