Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Maximum Power Transfer01:16

Maximum Power Transfer

278
Numerous practical applications within engineering disciplines, such as telecommunications, necessitate optimizing power delivery to a connected load. This pursuit, however, entails inherent internal losses, which can either equal or exceed the power supplied to the load. The Thevenin equivalent circuit is helpful in finding the maximum power a linear circuit can deliver to a load. It is assumed in this context that the load resistance can be adjusted.
By substituting the entire circuit with...
278
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

131
The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
131
Power Factor Correction01:20

Power Factor Correction

197
The power transmission to a factory involves the transfer of apparent power, a combination of active and reactive power. The power factor measures how effectively electrical power is converted into useful work output. The ratio of the real power (KW) that does the work to the apparent power (KVA) supplied to the circuit.
197
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

662
A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of...
662
Energy and Power Signals01:17

Energy and Power Signals

322
In an electrical system with a resistor, voltage and current signals facilitate the measurement of power and energy across the resistor. For a continuous-time signal, the total energy over a time interval is defined as the integral of the square of the signal's magnitude over that interval. Mathematically, this is expressed as:
322
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

666
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
666

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same authorSame journal

A Systematic Taxonomy of the Sunflower Optimization Algorithm: Variants, Hybridization Strategies, Applications, and Research Directions.

Biomimetics (Basel, Switzerland)·2026
Same author

Hybrid ANFIS-MPA and FFNN-MPA Models for Bitcoin Price Forecasting.

Biomimetics (Basel, Switzerland)·2025
See all related articles

Related Experiment Video

Updated: Jul 15, 2025

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware
08:13

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

Published on: December 25, 2017

8.2K

Training of Feed-Forward Neural Networks by Using Optimization Algorithms Based on Swarm-Intelligent for Maximum

Ebubekir Kaya1, Ceren Baştemur Kaya2, Emre Bendeş1

  • 1Department of Computer Engineering, Engineering Architecture Faculty, Nevsehir Haci Bektas Veli University, Nevşehir 50300, Türkiye.

Biomimetics (Basel, Switzerland)
|September 27, 2023
PubMed
Summary

Thirteen swarm-intelligent algorithms were evaluated for training artificial neural networks for maximum power point tracking. The firefly algorithm, selfish herd optimizer, and grasshopper optimization algorithm demonstrated superior performance in training and testing.

Keywords:
feed-forward neural networkmaximum power point trackingmetaheuristic algorithmswarm intelligence

More Related Videos

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

568
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

591

Related Experiment Videos

Last Updated: Jul 15, 2025

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware
08:13

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

Published on: December 25, 2017

8.2K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

568
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

591

Area of Science:

  • Artificial Intelligence
  • Renewable Energy Systems
  • Optimization Algorithms

Background:

  • Artificial neural networks (ANNs) are crucial for maximum power point tracking (MPPT) in renewable energy.
  • Effective MPPT relies heavily on the successful training of ANNs.
  • Metaheuristic algorithms, particularly swarm intelligence, are widely used for ANN training.

Purpose of the Study:

  • To evaluate and rank 13 swarm-intelligent optimization algorithms for training feed-forward neural networks.
  • To determine the most effective algorithms for achieving accurate maximum power point tracking.
  • To assess algorithm performance across different neural network structures using mean squared error.

Main Methods:

  • Utilized 13 swarm-intelligent algorithms: artificial bee colony, butterfly optimization, cuckoo search, chicken swarm optimization, dragonfly algorithm, firefly algorithm, grasshopper optimization algorithm, krill herd algorithm, particle swarm optimization, salp swarm algorithm, selfish herd optimizer, tunicate swarm algorithm, and tuna swarm optimization.
  • Trained feed-forward neural networks for MPPT using these algorithms.
  • Evaluated performance based on mean squared error (MSE) during training and testing phases.

Main Results:

  • The firefly algorithm, selfish herd optimizer, and grasshopper optimization algorithm were ranked as the top three most successful algorithms.
  • Achieved low training and testing mean squared errors: 4.5 × 10-4, 1.6 × 10-3, and 2.3 × 10-3 for training, and 4.6 × 10-4, 1.6 × 10-3, and 2.4 × 10-3 for testing, respectively.
  • Effective results were obtained with a low number of evaluations, indicating efficiency.

Conclusions:

  • The firefly algorithm, selfish herd optimizer, and grasshopper optimization algorithm are highly effective for ANN training in MPPT.
  • The evaluated swarm-intelligent algorithms generally provide acceptable results for MPPT applications.
  • These algorithms demonstrate significant potential for improving MPPT efficiency in renewable energy systems.