TY  - JOUR
AU  - Das, Yasmin
AU  - Ivanov, Jing
AU  - Karimi, Imran
AU  - Al-Rashid, Mohammad
TI  - Experimental Study of Low-Power CMOS Amplifiers
JO  - Open Access Journal of Engineering Applications
PY  - 2013
VL  - 37
IS  - 1
SP  - 157
EP  - 171
AB  - Environmental pressures and resource constraints have brought low-power CMOS amplifiers to the forefront of research on manufacturing processes. Field surveys of manufacturing processes provided the dataset to which adaptive neuro-fuzzy inference was subsequently applied. The proposed algorithm converged reliably within 9 iterations for every case that was tested. The results support the adoption of the proposed method in routine engineering practice.
KW  - low-power CMOS amplifiers
KW  - adaptive neuro-fuzzy inference
KW  - maximum power point tracking
KW  - arsenic removal
KW  - response surface methodology
SN  - 3105-7004
DO  - 10.55555/oajea.2013.3701.008
UR  - https://journal.bsaps.com/article/8975
ER  -