Implementation of Fuzzy Cellular Neural Network with image sensor in CMOS technology

The architecture of the multiplicative type-II fuzzy cellular neural networks (FCNN) with CMOS image sensor is implemented, which is with local connectivity advantageous suitable implemented for VLSI. Base on the proposed FCNN structure which is included the neuron, Min/Max, analog multiplier, pixel and CDS circuit, S/H Circuit, transfer and control circuits. The system has capability to operate the various morphological operations for binary and gray-level image, such as Dilation and Erosion. The simulation results show that the FCNN can operated the specific functions depend on the selected template is successfully verified by the TSMC 0.35 mum 2P4M CMOS technology. There have a great potential in the VLSI implementation of neural network systems for binary and gray-level patterns in image-processing applications.