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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

117
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
117
PD Controller: Design01:26

PD Controller: Design

383
In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...
383

You might also read

Related Articles

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

Sort by
Same author

Neural Network Model for Perceptual Evaluation of Product Modelling Design Based on Multimodal Image Recognition.

Computational intelligence and neuroscience·2022
Same journal

RETRACTION: Framework to Segment and Evaluate Multiple Sclerosis Lesion in MRI Slices Using VGG-UNet.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Facial Emotion Recognition Using a Novel Fusion of Convolutional Neural Network and Local Binary Pattern in Crime Investigation.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Intangible Cultural Heritage Reproduction and Revitalization: Value Feedback, Practice, and Exploration Based on the IPA Model.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: CNN Based Multiclass Brain Tumor Detection Using Medical Imaging.

Computational intelligence and neuroscience·2025
Same journal

RETRACTION: Distributed Scheduling Strategy of Virtual Power Plant Using the Particle Swarm Optimization Neural Network under Blockchain Background.

Computational intelligence and neuroscience·2025
See all related articles

Related Experiment Video

Updated: Oct 10, 2025

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

11.8K

A Product Styling Design Evaluation Method Based on Multilayer Perceptron Genetic Algorithm Neural Network Algorithm.

Jie Wu1

  • 1School of Anyang Institute of Technology, Anyang, Henan 45500, China.

Computational Intelligence and Neuroscience
|December 13, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a machine learning approach for industrial design, using a genetic algorithm-multilayer perceptron neural network (GA-MLP-NN) to create product shapes that meet consumer emotional needs and market demand.

More Related Videos

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.1K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.9K

Related Experiment Videos

Last Updated: Oct 10, 2025

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

11.8K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.1K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.9K

Area of Science:

  • Industrial Design
  • Human-Computer Interaction
  • Machine Learning

Background:

  • Consumer focus is shifting from product function to emotional experience.
  • Integrating computational methods into design processes is crucial for innovation.

Purpose of the Study:

  • To develop a novel method for evaluating and generating product shapes using machine learning.
  • To align product design with user emotional perception and market demand.

Main Methods:

  • Utilized a genetic algorithm-multilayer perceptron neural network (GA-MLP-NN) for shape evaluation.
  • Employed computer-aided design (CAD) for shape optimization and color scheme generation.
  • Established an interactive genetic modeling system incorporating user feedback.

Main Results:

  • Successfully generated product shapes aligned with user satisfaction and market demand.
  • Constructed a reliable mapping model between morphological features and cognitive-affective spaces.
  • Validated the model's performance for evaluating product design items.

Conclusions:

  • The GA-MLP-NN approach effectively quantifies product shape and optimizes design based on user emotion.
  • This method provides a reliable framework for generating market-driven product designs.
  • The integration of machine learning enhances the industrial design process by addressing user emotional aspects.