Architectural Design of Multiplicative type-II Fuzzy Cellular Neural Network in CMOS Technology

2007 ◽  
Author(s):  
Jui-Lin Lai ◽  
Yuan-Hung Lo ◽  
Rong-Jian Chen ◽  
Cheng-Fang Tai
Keyword(s):  
Neural Network ◽  
Architectural Design ◽  
Cellular Neural Network ◽  
Cmos Technology ◽  
Type Ii ◽  
Fuzzy Cellular Neural Network ◽  
Multiplicative Type

Fixed-time synchronization of memristor-based fuzzy cellular neural network with time-varying delay

2018 ◽  
Vol 355 (14) ◽  
pp. 6780-6809 ◽  
Author(s):  
Mingwen Zheng ◽  
Lixiang Li ◽  
Haipeng Peng ◽  
Jinghua Xiao ◽  
Yixian Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Synchronization ◽  
Fixed Time ◽  
Cellular Neural Network ◽  
Time Varying ◽  
Time Varying Delay ◽  
Fuzzy Cellular Neural Network ◽  
Varying Delay

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.

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