Modular Neural Network Architecture for Precise Condition Monitoring

2008 ◽  
Vol 57 (4) ◽  
pp. 805-812 ◽  
Author(s):  
H. Marzi
Keyword(s):  
Neural Network ◽  
Condition Monitoring ◽  
Network Architecture ◽  
Neural Network Architecture ◽  
Modular Neural Network ◽  
Precise Condition

A modular neural network architecture for inverse kinematics model learning

2001 ◽  
Vol 38-40 ◽  
pp. 797-805 ◽  
Author(s):  
Eimei Oyama ◽  
Arvin Agah ◽  
Karl F. MacDorman ◽  
Taro Maeda ◽  
Susumu Tachi
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Inverse Kinematics ◽  
Neural Network Architecture ◽  
Model Learning ◽  
Kinematics Model ◽  
Modular Neural Network

A Developed Convolutional Neural Network Architecture for Condition Monitoring

2021 ◽  
Author(s):  
Ibrahim Alqatawneh ◽  
Rongfeng Deng ◽  
Khalid Rabeyee ◽  
Zhang Chao ◽  
Fengshou Gu ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Condition Monitoring ◽  
Network Architecture ◽  
Neural Network Architecture

A MODULAR NEURAL NETWORK ARCHITECTURE FOR PATTERN CLASSIFICATION BASED ON DIFFERENT FEATURE SETS

1999 ◽  
Vol 09 (06) ◽  
pp. 563-581 ◽  
Author(s):  
KE CHEN ◽  
HUISHENG CHI
Keyword(s):  
Neural Network ◽  
Parameter Estimation ◽  
Pattern Classification ◽  
Network Architecture ◽  
Speaker Identification ◽  
Neural Network Architecture ◽  
Feature Sets ◽  
Modular Neural Network ◽  
Real World Problem ◽  
Comparative Results

We propose a novel connectionist method for the use of different feature sets in pattern classification. Unlike traditional methods, e.g., combination of multiple classifiers and use of a composite feature set, our method copes with the problem based on an idea of soft competition on different feature sets developed in our earlier work. An alternative modular neural network architecture is proposed to provide a more effective implementation of soft competition on different feature sets. The proposed architecture is interpreted as a generalized finite mixture model and, therefore, parameter estimation is treated as a maximum likelihood problem. An EM algorithm is derived for parameter estimation and, moreover, a model selection method is proposed to fit the proposed architecture to a specific problem. Comparative results are presented for the real world problem of speaker identification.

Sign in / Sign up

Export Citation Format

Share Document