✅作者简介：热爱科研的Matlab仿真开发者，修心和技术同步精进，matlab项目合作可私信。
🍎个人主页：Matlab科研工作室
🍊个人信条：格物致知。
更多Matlab仿真内容点击👇
⛄ 内容介绍
In this paper, we propose a novel adaptive kernel for the radial basis function neural networks. The proposed kernel adaptively fuses the Euclidean and cosine distance measures to exploit the reciprocating properties of the two. The proposed framework dynamically adapts the weights of the participating kernels using the gradient descent method, thereby alleviating the need for predetermined weights. The proposed method is shown to outperform the manual fusion of the kernels on three major problems of estimation, namely nonlinear system identification, patter classification and function approximation.
⛄ 部分代码
%% Nonlinear system identification using RBF Neural Networks
% Khan, S., Naseem, I., Togneri, R. et al. Circuits Syst Signal Process (2017) 36: 1639. doi:10.1007/s00034-016-0375-7
% https://link.springer.com/article/10.1007/s00034-016-0375-7
clc;
close all;
clear all;
%% Initialization of the simulation parameters
len = 1000; % Length of the signal
runs = 10; % Monte Carlo simulations
epochs = 100; % Number of times same signal pass through the RBF
learning_rate = 5e-4; % step-size of Gradient Descent Algorithm
noise_var=1e-1; % disturbance power / noise in desired outcome
h = [2 -0.5 -0.1 -0.7 3]; % system's coeffients
delays = 2; % order/delay/No.of.Taps
⛄ 运行结果
⛄ 参考文献
