Membership function circuit for neural/fuzzy hardware of analog-mixed operation based on the programmable conductance

This paper describes a way of implementing programmable analog membership function of fuzzy hardware, which is compatible to neural networks based on electronically programmable conductance. The theoretical model of electronic implementation is analyzed and verified by the measurement of CMOS test device and SPICE simulation. The analog membership function circuit is based on new input signal shaping circuit and neural networks circuit for Hodgkin-Huxley dynamic based neuron. The flexibility of programming is implemented by quadratic function of MOSFET in saturation region, and the Gaussian function by multiplying synapse circuit based on MOSFET in triode region. The circuit allows various membership functions with the linearity of 0.1%. The new membership function can realize the mixed hardware of fuzzy function and biologically plausible neural networks.