Neural Network Output Monitoring With Fixed Boundary Detection

The patent implements a feedback mechanism where the neural network's output is monitored by a boundary condition detector that identifies incorrect outputs. When an incorrect output is detected, the system retrieves relevant training data and re-trains the network, creating a closed-loop feedback system that continuously improves reliability through adaptive correction of errors.

The system performs self-correction by automatically detecting its own errors through boundary condition monitoring and autonomously re-training itself using stored training data. This self-service capability allows the neural network to improve its own reliability without external intervention, resolving the contradiction between maintaining correct output and adapting to reduce mistakes.

The patent pre-programs boundary conditions into the controller before operation begins. These boundary conditions serve as predetermined criteria for detecting incorrect outputs. During operation, the system simply checks whether outputs satisfy these pre-established boundary conditions, enabling reliable error detection without adding complex real-time analysis mechanisms.

The boundary condition detector acts as an intermediary component between the neural network and the system output. It monitors network outputs against predefined boundary conditions and triggers corrective actions when violations occur. This intermediary layer provides reliable error detection while maintaining relative system simplicity by decoupling the detection function from the core neural network architecture.

Instead of performing complete re-training operations frequently, the system applies partial re-training only when boundary conditions are violated. The re-training uses only the specific training data relevant to the detected error, rather than processing entire datasets. This partial action approach enables adaptive learning while minimizing time loss by avoiding unnecessary comprehensive re-training cycles.

The system discards incorrect outputs that violate boundary conditions and recovers by retrieving relevant training data from storage for targeted re-training. Rather than continuously processing all available data, the system selectively discards erroneous outputs and recovers correct behavior through focused re-training on pertinent training examples, reducing overall processing time while maintaining adaptability.