MATLAB Implementation of Least Mean Squares Algorithm for Pattern Recognition
MATLAB source code for Least Mean Squares algorithm implementation, featuring a classifier for pattern recognition applications with adaptive filter implementation
Application of Least Mean Square (LMS) Algorithm in Beamforming
Implementation of Least Mean Square (LMS) Algorithm in Beamforming Systems - LMS Algorithm Steps: 1. Variable and Parameter Definition: X(n) as input vector/training sample, W(n) as weight vector, b(n) as bias term, d(n) as desired output, y(n) as actual output, η as learning rate, n as iteration count. 2. Initialize weight vector W(0) with small random non-zero values, set n=0. 3. For input samples x(n) and desired output d, compute: e(n)=d(n)-X^T(n)W(n) followed by weight update W(n+1)=W(n)+ηX(n)e(n). 4. Check convergence criteria - terminate if satisfied, otherwise increment n and return to step 3. The algorithm demonstrates adaptive filter implementation for real-time beam pattern optimization.