A Comprehensive Review and Performance Analysis of Firefly Algorithm for Artificial Neural Networks

Nayak, Janmenjoy; Naik, Bighnaraj; Pelusi, Danilo; Krishna, A. Vamsi

doi:10.1007/978-3-030-28553-1_7

Cited by 9 publications

(5 citation statements)

References 50 publications

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“…The TLBO is adopted in the error compensation algorithm for the better optimized weights and biases of the neural network. The TLBO method [34,35,37] is a recently reported heuristic optimizing method that replicates the teaching and learning of human and is said to be better performance than the conventional back propagation method. The TLBO has two main phases: the teacher phase and the learner phase.…”

Section: Error Compensation With Tlbo Based Nnmentioning

confidence: 99%

“…In every interaction, the best solution will become the teacher and the students will change itself based on the teacher and the other students. The details of this method are fully described by Rao et al [34]. In this study, the TLBO is applied to optimize the weight and bias of the NN.…”

Section: Error Compensation With Tlbo Based Nnmentioning

confidence: 99%

“…Heuristic optimization methods are carried on optimizing the structure of the NN. Some of them are particle swam optimization, genetic algorithm, teaching learning optimization, firefly optimization [31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

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Robot Manipulator Calibration Using a Model Based Identification Technique and a Neural Network With the Teaching Learning-Based Optimization

Kang

2020

IEEE Access

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This paper proposes a new calibration method for enhancing robot positional accuracy of the industrial manipulators. By combining the joint deflection model with the conventional kinematic model of a manipulator, the geometric errors and joint deflection errors can be considered together to increase its positional accuracy. Then, a neural network is designed to additionally compensate the unmodeled errors, specially, non-geometric errors. The teaching-learning-based optimization method is employed to optimize weights and bias of the neural network. In order to demonstrate the effectiveness of the proposed method, real experimental studies are carried out on HH 800 manipulator. The enhanced position accuracy of the manipulator after the calibration confirms the feasibility and more positional accuracy over the other calibration methods.

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Section: Error Compensation With Tlbo Based Nnmentioning

confidence: 99%

Section: Error Compensation With Tlbo Based Nnmentioning

confidence: 99%

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Robot Manipulator Calibration Using a Model Based Identification Technique and a Neural Network With the Teaching Learning-Based Optimization

Kang

2020

IEEE Access

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“…Further applications of WPT‐based digital protection for IMDs involve integration into processing control stages, such as fuzzy logic and neural networks [27, 28]. The requirement for training and paradigm specification has caused a huge computational burden, which cast doubt regarding their online deployments.…”

Section: Introductionmentioning

confidence: 99%

Induction motor fault detection based on multi‐sensory control and wavelet analysis

Abubakar

Mekhilef

Gaeid³

et al. 2020

IET Electric Power Applications

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Detection and fault‐tolerant control (FTC) of faults in the early stage is desirable in improving efficiency. An implementation strategy is proposed for the individual controllers that work collectively in induction motor (IM) drive by interswitching from one form of a control strategy to another. The interswitching occurs between voltage by frequency (V/f) open‐loop control, closed‐loop (V/f) control, sensorless vector control and sensor vector control. Optimal performance capabilities are attained with vector control, whereas V/f is a setup that is affordable but with increased speed. In this study, the faults are open and short circuits winding faults, speed sensor failures and stator winding faults. When the severity of the fault is high, an embedded protection entity interrupts the motor. Daubechies 10 wavelet is used due to its significant vanishing moments compared to the other types of Daubechies as fault index with the stator current of 1 kW IM. A novel enhanced model reference adaptive system is employed for sensorless vector control to assess the motor speed. Both the simulation and experiment (using the F28335 DSP controller) indicate that the framework is effective in detecting the fault, ensuring the robustness of the FTC scheme and proving the effectiveness of the proposed algorithm.

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“…However, the conventional BPNN has some drawbacks such as getting stuck in local minima and slowing convergence [40]. To overcome these drawbacks, some heuristic algorithms have been used for training the network [41][42][43][44][45]. One among them is the Invasive Weed Optimization (IWO) algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Robotic Calibration Method Using a Model-Based Identification Technique and an Invasive Weed Optimization Neural Network Compensator

Kang

2020

Applied Sciences

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The study proposed a robotic calibration algorithm for improving the robot manipulator position precision. At first, the kinematic parameters as well as the compliance parameters of the robot can be identified together to improve its accuracy using the joint deflection model and the conventional kinematic model calibration technique. Then, an artificial neural network is constructed for further compensating the unmodeled errors. The invasive weed optimization is used to determine the parameters of the neural network. To show the advantages of the suggested technique, an HH800 robot is employed for the experimental study of the proposed algorithm. The improved position precision of the robot after the experiment firmly proves the practicability and positional precision of the proposed method over the other algorithms in comparison.

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A Comprehensive Review and Performance Analysis of Firefly Algorithm for Artificial Neural Networks

Cited by 9 publications

References 50 publications

Robot Manipulator Calibration Using a Model Based Identification Technique and a Neural Network With the Teaching Learning-Based Optimization

Robot Manipulator Calibration Using a Model Based Identification Technique and a Neural Network With the Teaching Learning-Based Optimization

Induction motor fault detection based on multi‐sensory control and wavelet analysis

A Robotic Calibration Method Using a Model-Based Identification Technique and an Invasive Weed Optimization Neural Network Compensator

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