Layered object recognition system using a hierarchical hybrid neural network architecture

1992 ◽  
Author(s):  
Srinivasan Raghavan ◽  
Naresh Gupta ◽  
Barbara A. Lambird ◽  
David Lavine ◽  
Laveen N. Kanall
Keyword(s):  
Neural Network ◽  
Object Recognition ◽  
Network Architecture ◽  
Recognition System ◽  
Neural Network Architecture ◽  
Hybrid Neural Network

FEEDFORWARD NEURAL NETWORK WITH ADAPTIVE REFERENCE PATTERN LAYER

1999 ◽  
Vol 09 (01) ◽  
pp. 1-9
Author(s):  
MIKKO LEHTOKANGAS
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Neural Network Architecture ◽  
Reference Pattern ◽  
Hybrid Neural Network ◽  
Rbf Networks ◽  
Pattern Layer ◽  
Input Variables ◽  
Hidden Layer ◽  
Efficient Learning

A hybrid neural network architecture is investigated for modeling purposes. The proposed hybrid is based on the multilayer perceptron (MLP) network. In addition to the usual hidden layers, the first hidden layer is selected to be an adaptive reference pattern layer. Each unit in this new layer incorporates a reference pattern that is located somewhere in the space spanned by the input variables. The outputs of these units are the component wise-squared differences between the elements of a reference pattern and the inputs. The reference pattern layer has some resemblance to the hidden layer of the radial basis function (RBF) networks. Therefore the proposed design can be regarded as a sort of hybrid of MLP and RBF networks. The presented benchmark experiments show that the proposed hybrid can provide significant advantages over standard MLPs and RBFs in terms of fast and efficient learning, and compact network structure.

scholarly journals POWER SIGNAL DISTURBANCE CLASSIFICATION USING WAVELET BASED NEURAL NETWORK

2017 ◽  
Vol 25 (2) ◽  
pp. 205-217
Author(s):  
S. Suja ◽  
Jovitha Jerome
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Wavelet Transform ◽  
Network Architecture ◽  
Recognition System ◽  
Discrete Wavelet ◽  
Neural Network Architecture ◽  
The Third ◽  
Power Quality Disturbance ◽  
Main Components

In this paper, the power signal disturbances are detected using discrete wavelet transform (DWT) and categorized using neural networks. This paper presents a prototype of power quality disturbance recognition system. The prototype contains three main components. First a simulator is used to generate power signal disturbances. The second component is a detector which uses the technique of DWT to detect the power signal disturbances. DWT is used to extract disturbance features in the power signal. The third component is neural network architecture to classify the power signal disturbances.    

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