Implementation of pattern recognition algorithm based on RBF neural network

2002 ◽  
Author(s):  
Sophie Bouchoux ◽  
Vincent Brost ◽  
Fan Yang ◽  
Jean Claude Grapin ◽  
Michel Paindavoine
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Rbf Neural Network ◽  
Recognition Algorithm ◽  
Pattern Recognition Algorithm

scholarly journals The approach to the formation of the neural network architecture for pattern recognition

2016 ◽  
Vol 12 (2) ◽  
pp. 61-64 ◽  
Author(s):  
Vitaly M Tatyankin
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Network Architecture ◽  
Test Sample ◽  
Recognition Algorithm ◽  
Neural Network Architecture ◽  
Pattern Recognition Algorithm ◽  
The Neural Network

An approach to the formation of an efficient pattern recognition algorithm. Under efficiency, understood as a zero error, resulting in the identification of the images on the test sample. As a test sample is considered an open database of images of handwritten digits MNIST.

Discriminating Ambulation Using a Smart Sensing Plate

2009 ◽  
Author(s):  
Mark T. Elliott ◽  
Xianghong Ma ◽  
Peter N. Brett
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Pattern Recognition ◽  
Early Diagnosis ◽  
Time Series Data ◽  
Recognition Algorithm ◽  
Response To Therapy ◽  
Series Data ◽  
Pattern Recognition Algorithm ◽  
Gait Patterns

The automated sensing scheme described in this paper has the potential to automatically capture, discriminate and classify transients in gait. The mechanical simplicity of the walking platform offers advantages over standard force plates. There is less restriction on dimensions offering the opportunity for multi-contact and multiple steps. This addresses the challenge of patient targeting and the evaluation of patients in a variety of ambulatory applications. In this work the sensitivity of the distributive tactile sensing method has been investigated experimentally. Using coupled time series data from a small number of sensors, gait patterns are compared with stored templates using a pattern recognition algorithm. By using a neural network these patterns were interpreted classifying normal and affected walking events with an accuracy of just under 90%. This system has potential in gait analysis and rehabilitation as a tool for early diagnosis in walking disorders, for determining response to therapy and for identifying changes between pre and post operative gait.

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