Title :
MO-OTA based recurrent neural network for solving simultaneous linear equations
Author :
Ansari, Mohd Samar ; Rahman, Syed Atiqur
Author_Institution :
Dept. of Electron. Eng., A.M.U., Aligarh, India
Abstract :
A non-linear feedback neural network based CMOS compatible circuit to solve a system of simultaneous linear equations is presented. The circuit has an associated transcendental energy function that ensures fast convergence to the solution. The use of multi-output OTAs ensures that synaptic weight resistance are eliminated thereby reducing the circuit complexity over existing schemes. PSPICE simulation results are presented for two chosen sets of equations and are found to agree with the algebraic solutions.
Keywords :
CMOS analogue integrated circuits; circuit complexity; linear algebra; operational amplifiers; recurrent neural nets; CMOS-compatible circuit; MO-OTA; PSPICE simulation; algebraic solutions; circuit complexity; multioutput OTA; nonlinear feedback neural network; operational transconductance amplifier; recurrent neural network; simultaneous linear equations; synaptic weight resistance; transcendental energy function; Biological neural networks; Equations; Integrated circuit modeling; Mathematical model; Neurons; Simulation; Transconductance; Linear algebra; Linear equations; Multi-output OTA; Neural network applications; Neural network hardware; Non-linear circuits;
Conference_Titel :
Multimedia, Signal Processing and Communication Technologies (IMPACT), 2011 International Conference on
Conference_Location :
Aligarh
Print_ISBN :
978-1-4577-1105-3
DOI :
10.1109/MSPCT.2011.6150472
