Interframe principal feature extraction using a multi-layer feedforward neural network

1992 ◽  
pp. 1177-1180 ◽  
Author(s):  
C. Shang ◽  
K. Brown
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Feedforward Neural Network ◽  
Principal Feature

Classification and Feature Extraction

2018 ◽  
pp. 150-168
Keyword(s):  
Neural Network ◽  
Myocardial Infarction ◽  
Feature Extraction ◽  
Classification Performance ◽  
Feedforward Neural Network ◽  
Anterior Myocardial Infarction ◽  
Ecg Signals ◽  
Gas Molecule ◽  
Gas Molecules

In this chapter, the proposed optimization algorithm, kinetic gas molecule optimization (KGMO), that is based on swarm behaviour of gas molecules is applied to train a feedforward neural network for classification of ECG signals. Five types of ECG signals are used in this work including normal, supraventricular, brunch bundle block, anterior myocardial infarction (Anterior MI), and interior myocardial infarction (Interior MI). The classification performance of the proposed KGMO neural network (KGMONN) was evaluated on the Physiobank database and compared against conventional algorithms.

Operational Determination of the Activated Sludge Process Using Neural Networks

1992 ◽  
Vol 26 (9-11) ◽  
pp. 2461-2464 ◽  
Author(s):  
R. D. Tyagi ◽  
Y. G. Du
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Steady State ◽  
Activated Sludge ◽  
Feedforward Neural Network ◽  
Training Data ◽  
Activated Sludge Process

A steady-statemathematical model of an activated sludgeprocess with a secondary settler was developed. With a limited number of training data samples obtained from the simulation at steady state, a feedforward neural network was established which exhibits an excellent capability for the operational prediction and determination.

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