Prompt Description

This fully connected neural network adopts a hierarchical serial architecture, comprising five core layers: an input layer, three hidden layers, and an output layer. The overall structure is as follows: Input Layer (3D) → Hidden Layer 1 (512D) → Hidden Layer 2 (512D) → Hidden Layer 3 (256D) → Output Layer (200D) Each layer has a clear functional role, forming a complete feature processing pipeline of "feature expansion → feature deepening → feature refinement → target mapping": 1. Input Layer: Receives pre-processed 3D normalized input features, completes data format conversion, and provides a foundation for subsequent feature mapping. 2. Hidden Layer 1 (Feature Expansion Layer): Implements the mapping from a 3D low-dimensional input to a 512D high-dimensional feature space, fully expanding the feature space and mining the potential correlation information of the input parameters. 3. Hidden Layer 2 (Feature Deepening Layer): Maintains the 512D high-dimensional feature space, strengthens feature interaction through deep mapping, and further extracts core features related to the stress sequence. 4. Hidden Layer 3 (Feature Refinement Layer): Reduces the 512D features to 256D, eliminates redundant feature information, focuses on core effective features, and provides high-quality feature input for the output layer. 5. Output Layer: Maps the 256D core features to 200D, outputs the prediction results corresponding to the real stress sequence, and completes the target output of the regression task. A schematic diagram of the connected neural network is drawn based on the above description.