Design of Multi-Valued Cellular Neural Networks for Associative Memory
Zhong Zhang, Takuma Akiduki, Tetsuo Miyake, Takashi Imamura · 2006 SICE-ICASE International Joint Conference · 2006
This paper discusses the design of multi-valued output functions of cellular neural networks (CNNs) implementing associative memories. The output function of the CNNs is a piecewise linear function which consists of a saturation and non-saturation range. The new structure of the output function is defined, and is called the "basic waveform". The saturation ranges with n levels are generated by adding n-1 basic waveforms. Consequently, creating an associative memory of multi-valued patterns has been successful, and computer experiment results show the validity of the proposed method. The results of this research can expand the range of applications of CNNs as associative memories