Deep Belief Network Feature Importance Visualization

The patent introduces an intermediary layer of visualization tools and methods that mediate between the deep learning model's internal computations and external interpretability requirements. This intermediary layer translates hidden neural network computations into visual representations and feature importance metrics, allowing users to understand model decisions without exposing the complex internal architecture.

The patent replaces direct inspection of the black box model's internal mechanics with alternative mechanisms for understanding model behavior. Instead of trying to decipher the hidden computational layers directly, the system uses substitution methods such as feature importance algorithms, visualization techniques, and surrogate models that provide interpretability without requiring access to the model's internal mechanical processes.

The patent segments the prediction system into two distinct parts: a traditional safety performance function component that maintains ease of application and distribution assumptions, and a deep learning component that handles complex pattern recognition and improves accuracy. This segmentation allows each component to operate in its optimal regime while contributing to the overall system performance.

The patent merges the advantages of both traditional SPF methods and deep learning models into a hybrid system. The traditional SPF provides interpretability and ease of application, while the deep learning component adds accuracy by capturing non-linear relationships and random collision patterns. The combination leverages the strengths of both approaches while mitigating their individual weaknesses.