The Architecture and Circuital Implementation Scheme of a New Cell Neural Network for Analog Signal Processing

Youren Wang (Nanjing University of Aeronautics and Astronautics, China)

Zhiqiang Zhang (Nanjing University of Aeronautics and Astronautics, China)

Jiang Cui (Nanjing University of Aeronautics and Astronautics, China)

Abstract: It is a difficult problem that using cellular neural network to make up of analog signal processing circuit. This paper presented the architecture of new cellular neural network SCCNN for analog signal processing circuits, designed the neural cell circuit, and developed the evolutionary design method of the SCCNN based on selfadapting genetic algorithm. In the architecture of new cellular neural network SCCNN, each neural cell connects with four neighborhood neural cells, the neural cell circuit and signal transfer line between neural cells are controlled by programmable switches. The validity of the SCCNN architecture and the evolutionary design method are verified through digital simulation. The experimental results indicate that the SCCNN hardware is a universal cellular neural network for analog signal processing circuit, which can be used to make up of the analog signal amplifier, analog signal filter, digit logic circuit, DAC circuit and so on.

Keywords: DAC circuit, analog signal processing circuit, cellular neural network, evolutionary design

Categories: B.2.3, B.7.3, C.5.4