Behavior Prediction Model for Dynamic Parameter Optimization
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Solution Overview
Problem
Existing electronic control and monitoring systems often rely on hard-coded algorithms that are not optimized for specific implementations, requiring expertise to modify parameters for improved performance, and adaptive parameterization efficiency decreases with non-monotonic or interacting parameters.
Innovation Solution
The development of behavior prediction models that automatically determine a substantially optimal parameterization for system components by predicting operant characteristics based on state descriptions and parameterizations, using offline learning and online detection components to select and apply the best parameterization for monitoring logic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If hard-coded algorithms are used in electronic control systems, then the system structure is simple and easy to implement, but the system cannot be optimized for particular implementations and requires expert knowledge to modify parameters
Solution Approach 1:
The patent implements dynamic parameterization where the monitoring algorithm transitions from static hard-coded parameters to dynamically adjustable parameters that can be automatically optimized. The system uses adaptive parameterization that allows parameters to change based on system state and performance requirements, resolving the contradiction between simple implementation and optimization capability.
Solution Approach 2:
The system employs self-optimizing capabilities where the monitoring algorithm automatically adjusts its own parameters without requiring external expert intervention. Through automated parameter tuning and adaptive optimization, the system serves itself by identifying and applying optimal parameter configurations based on observed system behavior and performance metrics.
2Productivity
If adaptive parameterization is implemented to optimize monitoring algorithms, then system performance can be improved, but efficiency decreases when using multiple non-monotonic or interacting parameters
Solution Approach 1:
The patent segments the parameter optimization problem by dividing complex interacting parameters into smaller, more manageable subsets or groups. This segmentation allows the system to optimize parameters in a structured manner, reducing the computational complexity associated with handling multiple non-monotonic or interacting parameters while maintaining overall system performance.
Solution Approach 2:
The system dynamically changes parameters based on system state and performance feedback, transitioning from fixed parameter sets to adaptive parameter configurations. This parameter change capability allows the monitoring algorithm to maintain high efficiency by adjusting only the necessary parameters at each optimization step rather than re-evaluating all parameters simultaneously.
3Measurement precision
If expert knowledge is required to tweak parameters for improved algorithm performance, then parameter optimization can be achieved, but the system requires specialized expertise and increases operational complexity
Solution Approach 1:
The monitoring system implements self-optimization capabilities that automatically tweak parameters without requiring external expert knowledge. The system uses built-in optimization algorithms that analyze performance metrics and autonomously adjust parameters to improve algorithm performance, eliminating the need for specialized expertise in parameter tuning.
Solution Approach 2:
The system incorporates feedback mechanisms that continuously monitor algorithm performance and use this information to guide parameter adjustments. Through closed-loop feedback, the system automatically identifies performance bottlenecks and adjusts parameters accordingly, replacing manual expert intervention with automated feedback-driven optimization.
Data Source
AI summary
In at least some embodiments, a method comprises obtaining a state description associated with a system having a component. The method further comprises automatically obtaining a substantially optimal parameterization for the component based on one or more operant characteristics of the component predicted by a behavior prediction model using combinations of the system's state description and a set of possible parameterizations for the component.