Table of contents
The
principle of BP neural network
The definition of BP neural network
The basic structure of BP neural network The activation function
of BP neural network
,
the transfer function of BP neural network
The principle and steps of particle swarm algorithm
Improve and optimize BP based on particle swarm algorithm
Matlab code of neural network for wind power generation
download link: https://download.csdn.net/download/abc991835105/87738383
effect diagram
result analysis
outlook
reference
Summary
Generally, the heuristic algorithm is used to improve the BP neural network, which is changed into a three-layer BP neural network. This article uses the particle swarm algorithm to improve the BP neural network, and constructs a PSO-BP neural network model with eight inputs and one output. Accurate forecasting of wind power
The principle of BP neural network
Definition of BP neural network
The artificial neural network does not need to determine the mathematical equation of the mapping relationship between input and output in advance, but only learns certain rules through its own training, and obtains 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 feed-forward network trained by error backpropagation (referred to as error backpropagation). Its algorithm is called BP algorithm. Its basic idea is the gradient descent method. The mean square error of the actual output value and the expected output value is the smallest.
Basic structure of BP neural network
The basic BP algorithm includes two processes of signal forward propagation and error back propagation. That is, the calculation of the error output is performed in the direction from input to output, while the adjustment of weights and thresholds is performed in the direction from output to input. During forward propagation, the input signal acts on the output node through the hidden layer, and after nonlinear transformation, the output signal is generated. If it is true