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

A FULLY AUTOMATED OFFLINE HANDWRITING RECOGNITION SYSTEM INCORPORATING RULE BASED NEURAL NETWORK VALIDATED SEGMENTATION AND HYBRID NEURAL NETWORK CLASSIFIER

2004 ◽  
Vol 18 (07) ◽  
pp. 1267-1283 ◽  
Author(s):  
MOUMITA GHOSH ◽  
RANADHIR GHOSH ◽  
BRIJESH VERMA
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Solution Method ◽  
Handwriting Recognition ◽  
Recognition System ◽  
Neural Network Classifier ◽  
Hybrid Neural Network ◽  
Rule Based ◽  
Extraction Algorithm ◽  
Offline Handwriting Recognition

In this paper we propose a fully automated offline handwriting recognition system that incorporates rule based segmentation, contour based feature extraction, neural network validation, a hybrid neural network classifier and a hamming neural network lexicon. The work is based on our earlier promising results in this area using heuristic segmentation and contour based feature extraction. The segmentation is done using many heuristic based set of rules in an iterative manner and finally followed by a neural network validation system. The extraction of feature is performed using both contour and structure based feature extraction algorithm. The classification is performed by a hybrid neural network that incorporates a hybrid combination of evolutionary algorithm and matrix based solution method. Finally a hamming neural network is used as a lexicon. A benchmark dataset from CEDAR has been used for training and testing.

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 Deep rule-based classifier for finger knuckle pattern recognition system

2020 ◽  
Author(s):  
Abdelouahab Attia ◽  
Zahid Akhtar ◽  
Nour Elhouda Chalabi ◽  
Sofiane Maza ◽  
Youssef Chahir
Keyword(s):  
Pattern Recognition ◽  
Recognition System ◽  
Rule Based ◽  
Pattern Recognition System ◽  
Rule Based Classifier
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