Title :
Real-time design of FIR filters by feedback neural networks
Author :
Bhattacharya, D. ; Antoniou, A.
Author_Institution :
Dept. of Electr. & Comput. Eng., Victoria Univ., BC, Canada
fDate :
5/1/1996 12:00:00 AM
Abstract :
A Hopfield (1986) type neural network for the design of 1-D FIR filters is proposed. Given the frequency or amplitude response, the all-analog network computes the filter coefficients in real time. The network is simulated with HSPICE and examples are included to show that this is an efficient way of solving the approximation problem compared to the standard techniques for FIR filter design.
Keywords :
FIR filters; Hopfield neural nets; SPICE; analogue processing circuits; approximation theory; circuit analysis computing; frequency response; low-pass filters; 1D FIR filters; FIR filter design; HSPICE; Hopfield type neural network; all-analog network; amplitude response; approximation problem solution; feedback neural networks; filter coefficients; frequency response; network simulation; real-time design; Computational modeling; Computer networks; Finite impulse response filter; Frequency response; Hopfield neural networks; Linear programming; Neural networks; Neurofeedback; Neurons;
Journal_Title :
Signal Processing Letters, IEEE
