Wavelet Packet Decomposition for Feature Vector Extraction from Voltage Signals

Perform wavelet packet decomposition on collected voltage signals to extract feature vectors, followed by BP neural network training. Specifically, we can first process the voltage signals using wavelet packet decomposition, which decomposes the signals into sub-signals across different frequency bands. This decomposition can be implemented using functions like `wpdec` in MATLAB or `pywt.wavedec` in Python's PyWavelets library, where we specify the wavelet type (e.g., 'db4') and decomposition level. From these sub-signals, we extract feature vectors that represent key signal characteristics, such as energy, entropy, or statistical moments. These features can be calculated using algorithms like energy computation per frequency band or Shannon entropy measurements. Subsequently, we train a BP (Backpropagation) neural network on these feature vectors to build an accurate voltage signal prediction model. The network architecture typically involves an input layer matching the feature dimension, hidden layers with activation functions like sigmoid or ReLU, and an output layer for predictions. Training implementation includes forward propagation, error calculation using loss functions like MSE, and weight updates through gradient descent optimization. This approach enables full utilization of collected voltage signals and enhances signal understanding and prediction capabilities through model training. Code implementation would involve signal processing toolboxes for decomposition and deep learning frameworks (e.g., TensorFlow or PyTorch) for neural network construction and training.