基于 RBF 神经网络进行非线性系统识别附matlab代码

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⛄ 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

⛄ 完整代码

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