Target
The
principle of BP neural network
, the definition of BP neural network,
the basic structure of BP neural network,
the neurons of BP neural network
, the activation function of BP neural network,
the transfer function of BP neural network,
wavelet neural network (BP neural network with wavelet basis as the transfer function )
Code link: Short-term passenger flow prediction based on wavelet neural network, WNN neural network short-term passenger flow prediction_Short-term energy analysis resources-CSDN library https://download.csdn.net/download/abc991835105/88139066
Neural network parameters
Passenger flow forecast based on wavelet neural network Rendering
diagram
Result analysis
Outlook
back view
The development of artificial intelligence is getting faster and faster. This article uses wavelet neural network to predict passenger flow.
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 mean square error of the actual output value and the expected output value is the smallest.
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 acts on the output node through the hidden layer and undergoes nonlinear transformation to generate an output signal. If the actual output does not match the expected output, the output signal is converted to