Interactive pattern classification by means of artificial neural networks

2002 ◽  
Author(s):  
M. Betrouni ◽  
S. Delsert ◽  
D. Hamad
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Pattern Classification ◽  
Artificial Neural

Real-time fMRI pattern-classification using artificial neural networks

2007 ◽  
Vol 118 (4) ◽  
pp. e114 ◽  
Author(s):  
M. Weygandt ◽  
R. Stark ◽  
C. Blecker ◽  
B. Walter ◽  
D. Vaitl
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real Time ◽  
Pattern Classification ◽  
Artificial Neural

Pattern classification with artificial neural networks: classification of algae, based upon flow cytometer data

1992 ◽  
Vol 258 (1) ◽  
pp. 11-25 ◽  
Author(s):  
J.R.M. Smits ◽  
L.W. Breedveld ◽  
M.W.J. Derksen ◽  
G. Kateman ◽  
H.W. Balfoort ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Pattern Classification ◽  
Flow Cytometer ◽  
Artificial Neural

scholarly journals Wavelet denoising of partial discharge signals and their pattern classification using artificial neural networks and support vector machines

DYNA ◽  
2017 ◽  
Vol 84 (203) ◽  
pp. 240-248 ◽  
Author(s):  
Ian Carlo Guzmán ◽  
Jose Luis Oslinger ◽  
Ruben Darío Nieto
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Support Vector Machines ◽  
Pattern Classification ◽  
Partial Discharge ◽  
Wavelet Denoising ◽  
Support Vector ◽  
Vector Machines ◽  
Artificial Neural

Este artículo presenta dos enfoques de reconocimiento de patrones usando huellas dactilares de descargas parciales como características de entrada para llevar a cabo la clasificación de patrones de DP. Un perceptrón multicapa (MLP) basado en el algoritmo de propagación hacia atrás y una máquina de soporte vectorial fueron entrenados para reconocer tres tipos de patrones de DP. Los resultados experimentales demostraron que los algoritmos pueden arrojar altas tasas de reconocimiento. De otra parte, la trasformada wavelet discreta (DWT) fue utilizada para eliminar el nivel de ruido presente en las DP como una etapa previa al proceso de clasificación. Diferentes wavelets madre fueron probadas a diferentes niveles de descomposición con el objeto de encontrar parámetros wavelet apropiados para obtener una mejor relación señal-ruido (SNR) y menos distorsión después del proceso de filtrado.

scholarly journals Factor Knowledge Mining Using the Techniques of AI Neural Networks and Self-Organizing Map

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Pao-Kuan Wu ◽  
Tsung-Chih Hsiao
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Pattern Classification ◽  
Hybrid Technique ◽  
Self Organizing Map ◽  
Knowledge Mining ◽  
Classification Techniques ◽  
Artificial Neural ◽  
Self Organizing

This paper offers a hybrid technique combined by artificial neural networks (ANN) and self-organizing map (SOM) as a way to explore factor knowledge. ANN and SOM are two kinds of pattern classification techniques based on supervised and unsupervised mechanisms, respectively. This paper proposes a new aspect to combine ANN and SOM as NNSOM process in order to delve into factor knowledge other than pattern classification. The experimental material is conducted by the investigation of street night market in Taiwan. NNSOM process can yield two results about factor knowledge: first, which factor is the most important factor for the development of street night market; second, what value of this factor is most positive to the development of street night market. NNSOM process can combine the advantages of supervised and unsupervised mechanisms and be applied to different disciplines.

scholarly journals NEURAL NETWORKS IN EXPERIMENTAL HIGH-ENERGY PHYSICS

1992 ◽  
Vol 03 (04) ◽  
pp. 733-771 ◽  
Author(s):  
C. BORTOLOTTO ◽  
A. DE ANGELIS ◽  
N. DE GROOT ◽  
J. SEIXAS
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Artificial Neural Networks ◽  
Pattern Classification ◽  
High Energy Physics ◽  
High Energy ◽  
Classification Problems ◽  
The Status ◽  
Artificial Neural ◽  
Energy Physics

During the last years, the possibility to use Artificial Neural Networks in experimental High Energy Physics has been widely studied. In particular, applications to pattern recognition and pattern classification problems have been investigated. The purpose of this article is to review the status of such investigations and the techniques established.

Realtime fMRI pattern-classification using artificial neural networks

2006 ◽  
Vol 37 (01) ◽  
Author(s):  
M Weygandt ◽  
R Stark ◽  
C Blecker ◽  
B Walter ◽  
D Vaitl
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Pattern Classification ◽  
Artificial Neural

Analysis on Pattern Classification using Artificial Neural Networks

2016 ◽  
Vol 1 (2) ◽  
Author(s):  
S. Kumar ◽  
S. Singh ◽  
V K Mishra
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Pattern Classification ◽  
Character Recognition ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Back Propagation Algorithm ◽  
Feed Forward Neural Network ◽  
Artificial Neural

Artificial neural networks (ANN) is one of the most dynamic research and application areas for pattern classification. ANN is the branch of Artificial Intelligence (AI). The network is trained by 'n' number of algorithm like back propagation algorithm. The different combinations of performance functions are used for training the ANN. The back propagation neural network (BPNN) can be used as a highly successful algorithm for pattern classification with suitable combination of performance functions while training and learning ANN. When the maximum likelihood algorithm was compared with back propagation neural network method, the BPNN was more accurate than other algorithms. A Multilayer feed-forward neural network algorithm is also used for pattern classification. However BPNN gives more effective results than other pattern classification algorithms. Handwriting Recognition (or HWR) is the ability of a machine to receive and interpret handwritten input from different sources like paper documents, photographs, touch-screens and other input devices. Various performance functions is examined in this paper so as to get to a conclusion that which function would be better for usage in the network to produce an efficient and effective system. The training of back propagation neural network is done with the application of Offline Handwritten Character Recognition.

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