Investigating three learning algorithms of a neural networks during inverse kinematics of robots

Ghafil, Hazim Nasir; Kovács, László; Jármai, Károly

doi:10.1201/9780367824037-5

Cited by 3 publications

(1 citation statement)

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“…The authors show a successful detection of failures using these methods, using data collected from control program's logged files. Ghafil et al 22 attempt to solve inverse kinematics of 3-DOF robotic manipulator using Levenber-Marquardt, Bayesian regularization, and scaled conjugate gradient learning algorithms. Results show that the best results are provided when using the Bayesian regularization algorithm.…”

Section: State-of-the-artmentioning

confidence: 99%

Utilization of multilayer perceptron for determining the inverse kinematics of an industrial robotic manipulator

Šegota

Anđelić

Mrzljak

et al. 2021

International Journal of Advanced Robotic Systems

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Inverse kinematic equations allow the determination of the joint angles necessary for the robotic manipulator to place a tool into a predefined position. Determining this equation is vital but a complex work. In this article, an artificial neural network, more specifically, a feed-forward type, multilayer perceptron (MLP), is trained, so that it could be used to calculate the inverse kinematics for a robotic manipulator. First, direct kinematics of a robotic manipulator are determined using Denavit–Hartenberg method and a dataset of 15,000 points is generated using the calculated homogenous transformation matrices. Following that, multiple MLPs are trained with 10,240 different hyperparameter combinations to find the best. Each trained MLP is evaluated using the R 2 and mean absolute error metrics and the architectures of the MLPs that achieved the best results are presented. Results show a successful regression for the first five joints (percentage error being less than 0.1%) but a comparatively poor regression for the final joint due to the configuration of the robotic manipulator.

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Section: State-of-the-artmentioning

confidence: 99%

Utilization of multilayer perceptron for determining the inverse kinematics of an industrial robotic manipulator

Šegota

Anđelić

Mrzljak

et al. 2021

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

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Applying clustered artificial neural networks to enhance contaminant diffusion prediction in geotechnical engineering

Alsamia,

Koch

2024

Sci Rep

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This paper introduces a novel approach using Clustered Artificial Neural Networks (CLANN) to address the challenge of developing predictive models for multimodal dataset with extreme parameter values. The CLANN method strategically decomposes the dataset, derived from Finite Element Analysis (FEA), into clusters, each representing distinct diffusion behaviors, and applies specialized neural networks within these clusters. The CLANN model was rigorously evaluated and demonstrated superior accuracy and consistency compared to traditional methods such as the Adaptive Neuro-Fuzzy Inference System (ANFIS) and fuzzy expert systems. While these conventional models struggled to capture the full range of diffusion dynamics, particularly under extreme conditions, CLANN consistently provided predictions that closely aligned with the actual FEA data across all scenarios. The versatility of the CLANN approach extends beyond its application to soil contamination. Its ability to handle complex, multimodal datasets suggests that this methodology can be generalized to a wide range of scientific and engineering problems characterized by similar data structures. This makes CLANN not only a powerful tool for geotechnical engineers but also a promising framework for broader applications where traditional models fall short. The findings of this study pave the way for more accurate, reliable, and adaptable predictive modeling in diverse domains, enhancing our ability to manage and mitigate environmental and engineering challenges.

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Adaptive Exploration Artificial Bee Colony for Mathematical Optimization

Alsamia,

Koch,

Albedran

et al. 2024

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The artificial bee colony (ABC) algorithm is a famous swarm intelligence method utilized across various disciplines due to its robustness. However, it exhibits limitations in exploration mechanisms, particularly in high-dimensional or complex landscapes. This article introduces the adaptive exploration artificial bee colony (AEABC), a novel variant that reinspires the ABC algorithm based on real-world phenomena. AEABC incorporates new distance-based parameters and mechanisms to correct the original design, enhancing its robustness. The performance of AEABC was evaluated against 33 state-of-the-art metaheuristics across twenty-five benchmark functions and an engineering application. AEABC consistently outperformed its counterparts, demonstrating superior efficiency and accuracy. In a variable-sized problem (n = 10), the traditional ABC algorithm converged to 3.086 × 106, while AEABC achieved a convergence of 2.0596 × 10−255, highlighting its robust performance. By addressing the shortcomings of the traditional ABC algorithm, AEABC significantly advances mathematical optimization, especially in engineering applications. This work underscores the significance of the inspiration of the traditional ABC algorithm in enhancing the capabilities of swarm intelligence.

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Investigating three learning algorithms of a neural networks during inverse kinematics of robots

Cited by 3 publications

References 1 publication

Utilization of multilayer perceptron for determining the inverse kinematics of an industrial robotic manipulator

Utilization of multilayer perceptron for determining the inverse kinematics of an industrial robotic manipulator

Applying clustered artificial neural networks to enhance contaminant diffusion prediction in geotechnical engineering

Adaptive Exploration Artificial Bee Colony for Mathematical Optimization

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