Self-organizing neural nets and the perceptual origin of the circle of fifths

1997 ◽  
pp. 169-180
Author(s):  
Nicola Cufaro Petron ◽  
Matteo Tricarico
Keyword(s):  
Neural Nets ◽  
Self Organizing

Segmentation of the Portuguese Clients of Pousadas de Portugal

2002 ◽  
pp. 70-88 ◽  
Author(s):  
Margarida G.M.S. Cardoso ◽  
Fernando Moura-Pires
Keyword(s):  
Market Segmentation ◽  
A Priori ◽  
Neural Nets ◽  
Self Organizing Maps ◽  
Self Organizing

The aim of our work is to perform a market segmentation of the clients of Pousadas de Portugal, a network for over 40 high-end small hotels, ENATUR. The data for this work was provided by a sample of more than 2500 clients that filled in a given questionnaire. The segmentation is based on how often the clients used the hotels, and on the type of stay they were seeking. A few different techniques were used: mixed approaches using a-priori constitution of clusters and/or neural nets (SOM – Self-Organizing Maps) and/or k-means. Profiling the obtained segments adds some new insights about the clients and helps ENATUR managers to better support new marketing decisions.

Combining self-organizing neural nets with multivariate statistics for efficient color image retrieval

2006 ◽  
Vol 102 (3) ◽  
pp. 250-258 ◽  
Author(s):  
Christos Theoharatos ◽  
Nikolaos Laskaris ◽  
George Economou ◽  
Spiros Fotopoulos
Keyword(s):  
Image Retrieval ◽  
Multivariate Statistics ◽  
Color Image ◽  
Neural Nets ◽  
Self Organizing

Self-Organizing Neural Nets: A New Approach to Quality in Textiles

1995 ◽  
Vol 65 (4) ◽  
pp. 196-202 ◽  
Author(s):  
S. Sette ◽  
L. Boullart ◽  
P. Kiekens
Keyword(s):  
Neural Nets ◽  
New Approach ◽  
Self Organizing

Modelling of anaerobic digestion using self-organizing maps and artificial neural networks

2000 ◽  
Vol 41 (12) ◽  
pp. 149-156 ◽  
Author(s):  
P. Holubar ◽  
L. Zani ◽  
M. Hagar ◽  
W. Fröschl ◽  
Z. Radak ◽  
...  
Keyword(s):  
Anaerobic Digestion ◽  
Production Rate ◽  
Regression Coefficient ◽  
Gas Production ◽  
Neural Nets ◽  
Organic Loading Rate ◽  
Self Organizing Map ◽  
Gas Composition ◽  
Volatile Fatty Acid Concentration ◽  
Self Organizing

In this work the training of a self-organizing map and a feed-forward back-propagation neural network was made. The aim was to model the anaerobic digestion process. To produce data for the training of the neural nets an anaerobic digester was operated at steady state and disturbed by pulsing the organic loading rate. Measured parameters were: gas composition, gas production rate, volatile fatty acid concentration, pH, redox potential, volatile suspended solids and chemical oxygen demand of feed and effluent. It could be shown that both types of self-learning networks in principle could be used to model the process of anaerobic digestion. Using the unsupervised Kohonen self-organizing map, the model's predictions could not follow the measurements in all details. This resulted in an unsatisfactory regression coefficient of R2= 0.69 for the gas composition and R2= 0.76 for the gas production rate. When the supervised FFBP neural net was used the training resulted in more precise predictions. The regression coefficient was found to be R2= 0.74 for the gas composition and R2== 0.92 for the gas production rate.

Automatic Indexing of a Sound Database Using Self-Organizing Neural Nets

1994 ◽  
Vol 18 (3) ◽  
pp. 53 ◽  
Author(s):  
Bernhard Feiten ◽  
Stefan Gunzel
Keyword(s):  
Neural Nets ◽  
Automatic Indexing ◽  
Self Organizing
Sign in / Sign up

Export Citation Format

Share Document