BP neural network principle, denoising algorithm based on BP neural network, modulated signal denoising based on BP neural network

Table of contents

Principle of BP neural network
Definition of BP neural network
Basic structure of BP neural network
BP neural network Neurons of the network
Activation function of BP neural network,
Transfer function of BP neural network
Principle of genetic algorithm Outlook Result analysis Renderings Matlab programming implementation data The complete code includes data download link: Denoising algorithm based on BP neural network, based on BP Neural network modulated signal denoising (complete code, complete data) resources - CSDN library https://download.csdn.net/download/abc991835105/88565869 Genetic algorithm flow chart
Main parameters of genetic algorithm

Summary

BP neural network, regression analysis, denoising algorithm based on BP neural network, modulated signal denoising based on BP neural network

The principle of BP neural network

Definition of BP neural network

Artificial neural networks do not need to determine the mathematical equations of the mapping relationship between input and output in advance. They only learn a certain rule through their own training and obtain the result closest to the expected output value when the input value is given. As an intelligent information processing system, the core of artificial neural network to realize its function is algorithm. BP neural network is a multi-layer feedforward network trained by error backpropagation (referred to as error backpropagation). Its algorithm is called BP algorithm. Its basic idea is the gradient descent method, which uses gradient search technology in order to make the network The error mean square error between the actual output value and the expected output value is the smallest. The BP neural network is a mature neural network with a large number of training transfer functions.

The basic structure of BP neural network

The basic BP algorithm includes two processes: forward propagation of signals and back propagation of errors. That is, the error output is calculated in the direction from input to output, while the weights and thresholds are adjusted in the direction from output to input. During forward propagation, the input signal passes through the hidden layer