Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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...

Calibration Curves: Linear Least Squares

Calibration Curves: Linear Least Squares

A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...

Classification of Systems-II

Classification of Systems-II

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,

You might also read

Related Articles

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

Sort by

Same author

Associations Between a Surrogate Index of Insulin Resistance and Hyperuricemia in Young and Middle-Aged Patients With Type 2 Diabetes Mellitus.

Journal of diabetes research·2026

Same author

SUDA: Simultaneous unsupervised knowledge distillation and adaptation of foundation models for efficient pathological image analysis.

Medical image analysis·2026

Same author

Differential analysis of CACNA1C polymorphisms among patients with depression and bipolar disorder and exploration of sleep as a mediator.

Journal of affective disorders·2026

Same author

Reply to letter regarding the article "Large-scale plasma proteomics for predicting future cardiovascular and all-cause mortality among individuals with cardiovascular-kidney-metabolic syndrome stage 0-3".

Metabolism: clinical and experimental·2026

Same author

Phase Segregation of Colloidal Quantum Dots Driven by Marangoni Vortex Flow for Multi-Component Microfabrication.

Journal of the American Chemical Society·2026

Same author

Zhurong rover reveals salt weathering-driven surface modification by transient brine activity on Mars.

Science bulletin·2026

Same journal

RETRACTION: Real-Time Modulation of Physical Training Intensity Based on Wavelet Recursive Fuzzy Neural Networks.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: Multidimensional Heterogeneous Network Link Adaptation Based on Mobile Environment.

Computational intelligence and neuroscience·2026

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

See all related articles

Search research articles

Related Experiment Video

Updated: Apr 25, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Study on optimized Elman neural network classification algorithm based on PLS and CA.

Weikuan Jia¹, Dean Zhao¹, Tian Shen¹

¹School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China.

Computational Intelligence and Neuroscience

|August 29, 2014

Summary

This summary is machine-generated.

This study introduces an optimized Elman neural network classification algorithm using partial least squares (PLS) and cluster analysis (CA). The PLS-CA-Elman algorithm enhances operating efficiency and recognition accuracy for high-dimensional data.

More Related Videos

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

Published on: November 8, 2019

Related Experiment Videos

Last Updated: Apr 25, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

Published on: November 8, 2019

Area of Science:

Machine Learning
Data Science
Artificial Intelligence

Background:

High-dimensional datasets with many features and samples present challenges like storage, computation, and reduced accuracy in Elman neural networks.
Correlative or repetitive factors in features and samples can negatively impact Elman neural network performance.

Purpose of the Study:

To develop an optimized Elman neural network classification algorithm for high-dimensional data.
To improve operating efficiency and recognition accuracy by addressing issues with large datasets and complex feature spaces.

Main Methods:

Partial Least Squares (PLS) was used for feature dimension reduction, eliminating correlative and repetitive factors.
Cluster Analysis (CA) was employed to eliminate correlative and repetitive factors within samples.
Each subclass was treated as a training sample to train distinct neural network models for improved precision.

Main Results:

The PLS-CA-Elman algorithm effectively reduces dimensions and handles sample correlations.
The method demonstrates unique advantages for small subclasses with high-dimensional features.
Optimized Elman neural network models were trained for subclass-specific recognition.

Conclusions:

The novel PLS-CA-Elman algorithm significantly enhances operating efficiency and recognition accuracy for high-dimensional datasets.
The approach shows superiority in classification tasks involving complex data structures.
The algorithm is recommended for further promotion and application in relevant fields.