Title :
Learning in neuro/fuzzy analog chips
Author :
Rodríguez-Vázquez, Angel ; Vidal-Verdú, Fernando
Author_Institution :
Dept. of Analog Design, Edificio CICA, Sevilla, Spain
fDate :
30 Apr-3 May 1995
Abstract :
This paper focus on the design of adaptive mixed-signal fuzzy chips. These chips have parallel architecture and feature electrically-controllable surface maps. The design methodology is based on the use of composite transistors-modular and well suited for design automation. This methodology is supported by dedicated, hardware-compatible learning algorithms that combine weight-perturbation and outstar
Keywords :
analogue processing circuits; circuit CAD; fuzzy logic; fuzzy neural nets; learning (artificial intelligence); neural chips; parallel architectures; perturbation techniques; adaptive mixed-signal fuzzy chips; composite transistors; design automation; design methodology; electrically-controllable surface maps; hardware-compatible learning algorithms; neuro/fuzzy analog chips; outstar; parallel architecture; weight-perturbation; Analog computers; CMOS technology; Circuits; Design automation; Design methodology; Fuzzy control; Fuzzy systems; Inference algorithms; Parallel architectures; Very large scale integration;
Conference_Titel :
Circuits and Systems, 1995. ISCAS '95., 1995 IEEE International Symposium on
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-2570-2
DOI :
10.1109/ISCAS.1995.523895
