Title :
Analog VLSI implementation of adaptive neuro-fuzzy inference systems
Author :
Sultan, Ahmed ; El-Sayed, Mohamed
Author_Institution :
Dept. of Electr. Eng., Alexandria Univ., Egypt
Abstract :
This paper presents an analog VLSI implementation of adaptive neuro-fuzzy inference systems (ANFIS). Stochastic perturbative techniques, which are more VLSI friendly than standard learning techniques such as back-propagation, are used for on-chip learning. The system is tested by the task of predicting the Mackey-Glass chaotic time series. The system is built and simulated with SPICE using CMOS 1.2 μm N-well technological parameters with 5 V supply. The obtained results have shown how on-chip learning is very fast compared to software implemented learning algorithms
Keywords :
VLSI; analogue processing circuits; backpropagation; chaos; inference mechanisms; neural chips; time series; 1.2 micron; 5 V; Mackey-Glass chaotic time series; N-well technological parameters; adaptive neuro-fuzzy inference systems; analog VLSI implementation; back-propagation; on-chip learning; stochastic perturbative techniques; Adaptive systems; CMOS technology; Chaos; Computational intelligence; Glass; Neural networks; SPICE; Stochastic processes; System testing; Very large scale integration;
Conference_Titel :
Electronics, Circuits and Systems, 2000. ICECS 2000. The 7th IEEE International Conference on
Conference_Location :
Jounieh
Print_ISBN :
0-7803-6542-9
DOI :
10.1109/ICECS.2000.911601
