Skip to main navigation
Skip to search
Skip to main content
KAUST FACULTY PORTAL Home
Home
Profiles
Research units
Research output
Press/Media
Prizes
Courses
Equipment
Student theses
Datasets
Search by expertise, name or affiliation
An optimized recursive learning algorithm for three-layer feedforward neural networks for mimo nonlinear system identifications
Daohang Sha, Vladimir B. Bajic
Computer, Electrical and Mathematical Sciences and Engineering
Research output
:
Contribution to journal
›
Article
›
peer-review
5
Scopus citations
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'An optimized recursive learning algorithm for three-layer feedforward neural networks for mimo nonlinear system identifications'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Computer Science
Nonlinear System Identification
100%
Feedforward Neural Network
100%
Learning Algorithm
100%
Feed Forward Neural Networks
75%
Gradient Method
50%
Learning Rate
50%
Online Learning
25%
Backpropagation
25%
Simulation Experiment
25%
Matrix Operation
25%
Weak Convergence
25%
System Dynamics
25%
Engineering
Recursive
100%
Nonlinear System Identification
100%
Feedforward
100%
Learning Algorithm
100%
Simulation Experiment
25%
Matrix Operation
25%
Optimization Method
25%
Recursive Algorithm
25%
Determines
25%
Backpropagation
25%
Keyphrases
Mimo
100%
Nonlinear System Identification
100%
Recursive Training
100%
Weak Convergence
25%
Two-input
25%
Proper Learning
25%
Operation Method
25%
Chemical Engineering
Feedforward Neural Network
100%
Nonlinear System
100%
Backpropagation
25%