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

Classification of Systems-I01:26

Classification of Systems-I

728
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
728
Classification of Systems-II01:31

Classification of Systems-II

638
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
638
Aggregates Classification01:29

Aggregates Classification

1.0K
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
1.0K
Classification of Signals01:30

Classification of Signals

1.5K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.5K

You might also read

Related Articles

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

Sort by
Same journal

Multiphysics Investigation on Thermal Characteristics of Internal Bio-Inspired V-Ribbed Cooling Channels for Outer Rotor PMSM.

Biomimetics (Basel, Switzerland)·2026
Same journal

Smart Logistics Model for Supply Chain Management via Brain-Inspired Geometric Deep Networks.

Biomimetics (Basel, Switzerland)·2026
Same journal

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

Biomimetics (Basel, Switzerland)·2026
Same journal

Toward a Compositional Theory of Trust in Embodied Intelligence: A QNLP Framework for Modeling Context, Interaction, and Trustworthiness.

Biomimetics (Basel, Switzerland)·2026
Same journal

Empirical Logic for Bio-Inspired Soft Computing: Illustrative Applications in Control Engineering and Cluster Analysis.

Biomimetics (Basel, Switzerland)·2026
Same journal

A Modified Multi-Strategy Dhole Optimization Algorithm and Its Engineering Applications.

Biomimetics (Basel, Switzerland)·2026

Related Experiment Videos

Aquila Optimization-Assisted Artificial Neural Network for Classification Problems.

Gokhan Kayhan1, Seyma Hasbolat Unal1

  • 1Department of Computer Engineering, Ondokuz Mayis University, Kurupelit, Samsun 55139, Türkiye.

Biomimetics (Basel, Switzerland)
|April 27, 2026
PubMed
Summary

This study introduces the Aquila Optimizer ANN (AOANN), a novel hybrid model for optimizing Artificial Neural Networks (ANNs). AOANN enhances classification problem-solving by overcoming local optima challenges in parameter tuning.

Keywords:
aquila optimizerartificial neural networksclassification problemsmachine learningmetaheuristic optimization

Related Experiment Videos

Area of Science:

  • Computational intelligence
  • Machine learning
  • Artificial intelligence

Background:

  • Artificial Neural Networks (ANNs) are powerful tools for pattern recognition but face challenges with parameter optimization, often getting stuck in local optima.
  • Traditional optimization methods struggle to find optimal weight and bias values for ANNs, hindering network performance.
  • Heuristic algorithms are commonly employed for ANN training and optimization problems.

Purpose of the Study:

  • To propose a hybrid Artificial Neural Network model optimized with the Aquila Optimizer (AOANN).
  • To enhance the parameter optimization process for ANNs using a recent metaheuristic algorithm.
  • To evaluate the performance of the AOANN model in solving classification problems.

Main Methods:

  • The study utilized the Aquila Optimizer (AO), a recent metaheuristic algorithm, for parameter optimization of Artificial Neural Networks.
  • A hybrid model, termed AOANN, was developed by integrating the AO algorithm with an ANN architecture.
  • The AOANN model's performance was assessed using empirical datasets: Cancer, Iris, Glass, and Wine.

Main Results:

  • The proposed AOANN model demonstrated stable performance in classification tasks across multiple empirical datasets.
  • Comparison with established ANN models indicated the effectiveness of the AOANN approach.
  • The hybridization of the Aquila Optimizer with ANNs improved optimization performance.

Conclusions:

  • The AOANN model represents a stable and effective soft computation approach for classification problems.
  • Integrating metaheuristic algorithms like AO offers a promising direction for enhancing ANN performance.
  • The AOANN model successfully addresses the local optima issue in ANN parameter optimization.