Wavelet-Neural Network (WNN)-Go服务器开发

Wavelet-Neural Network (WNN) is a type of artificial neural network (ANN) that uses wavelet analysis to enhance the network’s ability to process time series data. This approach combines the strengths of both wavelet analysis and neural networks to achieve accurate predictions in various fields, such as finance, medicine, and engineering.

The WNN architecture consists of three layers: input layer, hidden layer, and output layer. The input layer receives the time series data, which is then processed by the hidden layer using wavelet analysis. The hidden layer extracts features from the time series data and generates a set of coefficients that are used as inputs for the output layer.

The output layer performs the final classification or regression task based on the input data and the extracted features. The network’s weights and biases are optimized using backpropagation, a standard procedure for training ANNs.

Wavelet analysis enables the WNN to analyze signals at different scales, both in time and frequency domains, providing a high level of accuracy in detecting patterns and extracting relevant information from time series data. Additionally, the WNN has the ability to learn from historical data, enabling it to make accurate predictions for future events.

Overall, the WNN is an effective tool for analyzing and predicting time series data, providing valuable insight into complex systems and processes.