Competitive neural network scheme for learning vector quantisation

1999 ◽  
Vol 35 (9) ◽  
pp. 725 ◽  
Author(s):  
Jung-Hua Wang ◽  
Chung-Yun Peng
Keyword(s):  
Neural Network ◽  
Competitive Neural Network ◽  
Learning Vector Quantisation ◽  
Vector Quantisation

scholarly journals GMSK NEURAL NETWORK BASED DEMODULATOR

2014 ◽  
pp. 71-76
Author(s):  
Andrea Aiello ◽  
Domenico Grimaldi ◽  
Sergio Rapuano
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Input Signal ◽  
Digital Signal Processor ◽  
Digital Signal ◽  
European Telecommunication Standard Institute ◽  
Learning Vector Quantisation ◽  
Vector Quantisation ◽  
Artificial Neural ◽  
Signal Processor

In this paper, the pattern recognition characteristics of the Artificial Neural Net­ works are used to realise a real demodulator for Gaussian Minimum Shift­Keying signals, used in the GSM telecommunications. The demodulator utilises the Learning Vector Quantisation (LVQ) neural network. It offers both greater efficiency in demodulating and less sensitivity to noise. In order to solve the problem regarding input signal synchronisation, a pre­processing phase is organised. The demodulator prototype has been realised by implementing the pre­processing phase and the LVQ neural network on TMS320C30 Digital Signal Processor. The demodulator has been tested according to the European Telecommunication Standard Institute Recommendations.

K-Means and Competitive Neural Network Based Clustering of Climate Data

2021 ◽  
Author(s):  
Sandeep Kumar Sunori ◽  
Sudhanshu Maurya ◽  
Amit Mittal ◽  
Kiran Patni ◽  
Shweta Arora ◽  
...  
Keyword(s):  
Neural Network ◽  
Climate Data ◽  
Competitive Neural Network

An Optimizing Method of Competitive Neural Network

2011 ◽  
Vol 467-469 ◽  
pp. 894-899
Author(s):  
Hong Men ◽  
Hai Yan Liu ◽  
Lei Wang ◽  
Yun Peng Pan
Keyword(s):  
Neural Network ◽  
Network Structure ◽  
Clustering Analysis ◽  
Learning Rate ◽  
Optimum Number ◽  
Number Of Clusters ◽  
Competitive Neural Network ◽  
Output Neurons ◽  
Simulation Results ◽  
Strong Ability

This paper presents an optimizing method of competitive neural network(CNN):During clustering analysis fixed on the optimum number of output neurons according to the change of DB value，and then adjusted connected weight including increasing ,dividing , delete. Each neuron had the different variety trend of learning rate according with the change of the probability of neurons. The optimizing method made classification more accurate. Simulation results showed that optimized network structure had a strong ability to adjust the number of clusters dynamically and good results of classification.

A COMPETITIVE NEURAL NETWORK APPROACH TO WEB-PAGE CATEGORIZATION

2001 ◽  
Vol 09 (06) ◽  
pp. 731-741 ◽  
Author(s):  
ZHI-QIANG LIU ◽  
YA-JUN ZHANG
Keyword(s):  
Neural Network ◽  
Real World ◽  
Gradient Descent ◽  
Web Pages ◽  
Learning Method ◽  
Network Approach ◽  
Web Page ◽  
Real World Data ◽  
Neural Network Approach ◽  
Competitive Neural Network

Recently many techniques, e.g., Google or AltaVista, are available for classifying well-organized, hierarchical crisp categories from human constructed web pages such as that in Yahoo. However, given the current rate of web-page production, there is an urgent need of classifiers that are able to autonomously classify web-page categories that have overlaps. In this paper, we present a competitive learning method for this problem, which based on a new objective function and gradient descent scheme. Experimental results on real-world data show that the approach proposed in this paper gives a better performance in classifying randomly-generated, knowledge-overlapped categories as well as hierarchical crisp categories.

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