Cellular Neural Network for Associative Memory and Its Application to Braille Image Recognition

2006 ◽  
Author(s):  
M. Namba ◽  
Z. Zhang
Keyword(s):  
Neural Network ◽  
Associative Memory ◽  
Image Recognition ◽  
Cellular Neural Network

scholarly journals Design Ratio-Memory Cellular Neural Network (RMCNN) in CMOS Circuit Used in Association-Memory Applications for 0.25 mm Silicon Technology

2016 ◽  
Vol 10 (1) ◽  
pp. 54-69
Author(s):  
Jui-Lin Lai ◽  
Chung-Yu Wu
Keyword(s):  
Neural Network ◽  
Associative Memory ◽  
Hebbian Learning ◽  
Learning Algorithm ◽  
Cellular Neural Network ◽  
Cmos Technology ◽  
Neural System ◽  
Test Input ◽  
Vlsi Technology ◽  
Memory Applications

The paper is proposed the Ratio-Memory Cellular Neural Network (RMCNN) that structure with the self-feedback and the modified Hebbian learning algorithm. The learnable RMCNN architecture was designed and realized in CMOS technology for associative memory neural network applications. The exemplar patterns can be learned and correctly recognized the output patterns for the proposed system. Only self-output pixel value in A template and B template weights are updated by the nearest neighboring five elements for all test input exemplar patterns. The learned ratio weights of the B template are generated that the catch weights are performed the summation of absolute coefficients operation to enhance the feature of recognized pattern. Simulation results express that the system can be learned some exemplar patterns with noise and recognized the correctly pattern. The 9×9 RMCNN structure with self-feedback and the modified Hebbian learning algorithm is implemented and verified in the CMOS circuits for TSMC 0.25 µm 1P5M VLSI technology. The proposed RMCNN have more learning and recognition capability for the variant exemplar patterns in the auto-associative memory neural system applications.

COLOR EDGE DETECTIONS BASED ON CELLULAR NEURAL NETWORK

2008 ◽  
Vol 18 (04) ◽  
pp. 1231-1242 ◽  
Author(s):  
GUODONG LI ◽  
LEQUAN MIN ◽  
HONGYAN ZANG
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Edge Detection ◽  
Image Recognition ◽  
Test Pattern ◽  
Color Image ◽  
Cellular Neural Network ◽  
Color Image Processing ◽  
Rgb Images ◽  
Rgb Image

Color edge detection is one of the most important steps for RGB image recognition. In this paper, we first present two robustness design theorems for the Edgegray Detection Cellular Neural Network (EDGE CNN) and the counter detection (CD) CNN. Second, based on a color plane transform of RGB image and the two theorems, we design a color EDGE CNN and a CD CNN. As applications, the two CNNs detect successfully the edges of a standard color edge test pattern, and two popular RGB images, respectively. Our findings show that CNN may provide a useful tool for color image processing.

A SIMPLE NEURAL NETWORK APPROACH TO INVARIANT IMAGE RECOGNITION

2001 ◽  
Vol 15 (01) ◽  
pp. 11-17
Author(s):  
M. ANDRECUT ◽  
M. K. ALI
Keyword(s):  
Neural Network ◽  
Distribution Function ◽  
Image Recognition ◽  
Network Architecture ◽  
Cellular Neural Network ◽  
Angle Distribution ◽  
Neural Network Architecture ◽  
Feed Forward Neural Network ◽  
Neural Network Approach ◽  
Simple Neural Network

In this paper we propose a simple neural network architecture for invariant image recognition. The proposed neural network architecture contains three specialized modules. The neurons from the first module are connected in a cellular neural network structure, which is responsible for image processing: edge detection and segmentation. The second module is a feed forward neural network for invariant feature extraction from the sensorial layer: computation of the pair distribution function and bond angle distribution function. The third module is responsible for image classification. An application to the face recognition problem is also presented.

Optimizing Time-multiplexing Raster Cellular Neural Network simulator using genetic algorithms with RK8(6)

2011 ◽  
Vol 3 (6) ◽  
pp. 87-90
Author(s):  
O. H. Abdelwahed O. H. Abdelwahed ◽  
◽  
M. El-Sayed Wahed ◽  
O. Mohamed Eldaken
Keyword(s):  
Neural Network ◽  
Genetic Algorithms ◽  
Cellular Neural Network ◽  
Network Simulator
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