MATLAB Implementation of Signal Adaptive Processing with Radial Basis Function

Signal adaptive processing is an effective signal processing method that automatically adjusts the processing procedure based on signal characteristics. In this approach, we employ the Gaussian radial basis function (RBF) as the kernel function, which offers excellent adaptability and flexibility. The Gaussian RBF can automatically tune its parameters according to data distribution patterns, leading to more accurate and reliable processing results. In MATLAB implementation, the key parameters include the kernel width (sigma) which controls the function's smoothness and influence range. The adaptive processing typically involves optimizing these parameters using techniques like cross-validation or gradient descent algorithms. The core MATLAB functions often utilized include 'fitrsvm' for regression tasks or 'fitcsvm' for classification, where the kernel function can be specified as 'gaussian' or 'rbf'. Implementing signal adaptive processing is crucial in signal processing applications as it significantly enhances processing efficiency and signal quality through dynamic parameter optimization and kernel-based learning mechanisms.