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

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

266
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
266
Neural Circuits01:25

Neural Circuits

2.2K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
2.2K

You might also read

Related Articles

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

Sort by
Same author

DANNET: deep attention neural network for efficient ear identification in biometrics.

PeerJ. Computer science·2025
Same author

Optimizing video data security: A hybrid MAES-ECC encryption technique for efficient internet transmission.

PloS one·2024
Same author

Terrorism group prediction using feature combination and BiGRU with self-attention mechanism.

PeerJ. Computer science·2024
Same author

Neuro-controller implementation for the embedded control system for mini-greenhouse.

PeerJ. Computer science·2023
Same author

Diagnosis of Chronic Ischemic Heart Disease Using Machine Learning Techniques.

Computational intelligence and neuroscience·2022
Same author

Development of Microcontroller-Based System for Background Radiation Monitoring.

Sensors (Basel, Switzerland)·2021
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Nov 23, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.7K

Optimal Artificial Neural Network Type Selection Method for Usage in Smart House Systems.

Vasyl Teslyuk1, Artem Kazarian1, Natalia Kryvinska2

  • 1Department of Automated Control Systems, Lviv Polytechnic National University, 79013 Lviv, Ukraine.

Sensors (Basel, Switzerland)
|December 30, 2020
PubMed
Summary
This summary is machine-generated.

This study proposes a method for selecting the best artificial neural network (ANN) for smart home systems. By optimizing for minimal error, the approach ensures accurate processing of fuzzy sensor data for improved control signals.

Keywords:
artificial neural network (ANN) algorithmfeedforward neural networkgated recurrent unitlong short-term memoryrecurrent neural networksmart house

More Related Videos

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.8K
A Fully Automated and Highly Versatile System for Testing Multi-cognitive Functions and Recording Neuronal Activities in Rodents
09:13

A Fully Automated and Highly Versatile System for Testing Multi-cognitive Functions and Recording Neuronal Activities in Rodents

Published on: May 3, 2012

14.6K

Related Experiment Videos

Last Updated: Nov 23, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.7K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.8K
A Fully Automated and Highly Versatile System for Testing Multi-cognitive Functions and Recording Neuronal Activities in Rodents
09:13

A Fully Automated and Highly Versatile System for Testing Multi-cognitive Functions and Recording Neuronal Activities in Rodents

Published on: May 3, 2012

14.6K

Area of Science:

  • Artificial Intelligence
  • Smart Home Technology
  • Control Systems

Background:

  • Smart home systems require processing complex, fuzzy input data from various sensors.
  • Artificial neural networks (ANNs) are commonly used for this data processing.
  • Different ANN architectures exhibit varying accuracies and capabilities for specific data types and control signal generation.

Purpose of the Study:

  • To develop a method for selecting the optimal artificial neural network (ANN) type for smart home systems.
  • To address the challenge of varying ANN performance with different data types and control signal generation.

Main Methods:

  • Proposed an optimization problem to determine the best ANN type.
  • Used the error of each ANN type in controlling a smart home subsystem as the optimization criterion.
  • Trained different ANN types using identical historical input data.

Main Results:

  • Identified dependencies between ANN types, network architecture (number of layers and neurons), and calculation errors.
  • Demonstrated how network configuration impacts the accuracy of control signal generation for smart home subsystems.
  • Quantified the error in parameter calculation relative to expected results for different ANNs.

Conclusions:

  • The proposed optimization method effectively identifies the most suitable ANN for specific smart home applications.
  • Understanding the relationship between ANN architecture and error is crucial for optimizing fuzzy data processing.
  • This research provides a framework for enhancing the performance and reliability of smart home control systems.