N-dimension geometry used in the design of a dynamic neural-network pattern-recognition system

2009 ◽  
Author(s):  
Chialun John Hu
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Recognition System ◽  
Dynamic Neural Network ◽  
Pattern Recognition System ◽  
Network Pattern

scholarly journals Artificial Auditory Perception Pattern Recognition System Based on Spatiotemporal Convolutional Neural Network

2019 ◽  
Vol 10 (1) ◽  
pp. 139
Author(s):  
Xia Fang ◽  
Han Fang ◽  
Zhan Feng ◽  
Jie Wang ◽  
Libin Zhou
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Convolutional Neural Network ◽  
Intelligent System ◽  
Human Perception ◽  
Principal Component ◽  
Recognition System ◽  
Stepper Motor ◽  
Pattern Recognition System ◽  
Perception System

It is difficult to combine human sensory cognition with quality detection to form a pattern recognition system based on human perception. In the future, miniature stepper motor modules will be widely used in advanced intelligent equipment. However, the reducer module based on powder metallurgy parts and the stepper motor may have various defects during operation, with varying definitions of those that affect the user comfort. It is tremendously important to develop an intelligent system to effectively simulate human senses. In this work, an elaborated personification of the perceptual system is proposed to simulate the ventral and flow of the human perception system: two branch systems consisting of a spatiotemporal convolutional neural network (S-CNN) and a concatenated HoppingNet temporal convolutional neural network (T-CNN). To ensure high robustness of the system, we combined principal component analysis (PCA) with the opinions of an experienced quality control (QC) team members to screen the data, and used a bionic ear to simulate human perception characteristics. After repeated comparisons of the tester, the results show that our anthropoid pattern sensing system has high accuracy and robustness for a stepper motor module.

scholarly journals Hybrid neural network and rule-based pattern recognition system capable of self-modification

1990 ◽  
Author(s):  
Charles W. Glover ◽  
Mike Silliman ◽  
Mark Walker ◽  
Phil Spelt ◽  
Nageswara S. V. Rao
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Recognition System ◽  
Hybrid Neural Network ◽  
Rule Based ◽  
Pattern Recognition System
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