Analog Sequential Architecture for Neuro-Fuzzy Models VLSI Implementation

An analog sequential architecture for efficient neuro-fuzzy models implementation is proposed. The best features of digital and analog domains are combined to provide a high degree of flexibility (in terms of number of inputs, number of membership functions per input and number of fuzzy rules) when handling real world tasks. The performance estimations show a good area/throughput ratio, thus making the architecture suitable for a wide range of applications.