Cost Function Modeling for Autonomous System Parameter Optimization
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Solution Overview
Problem
Determining optimized system parameters for complex technical systems is challenging due to the lack of clear, reliable cost functions and the need for expert knowledge, making the process costly and time-consuming, especially when multiple output values complicate the interpretation of system quality.
Innovation Solution
A computer-implemented method that automatically determines optimized system parameters by defining a function space, applying random parameters, modeling the system using statistical analysis, generating probability functions based on rules, and combining them to maximize the overall probability of satisfying the cost function, thereby optimizing system parameters without requiring expert intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expert knowledge and manual determination methods are used to determine system parameters, then the quality and reliability of parameter determination can be maintained, but the process becomes costly and time-consuming
Solution Approach 1:
The system performs self-parameterization by automatically determining cost functions and optimizing system parameters without requiring expert intervention. The automated system uses machine learning models to learn from system behavior and autonomously determine optimal parameters, replacing the manual expert process while maintaining determination accuracy.
Solution Approach 2:
The patent replaces the manual expert process with an automated computational system using machine learning and optimization algorithms. The mechanical/manual process of expert analysis is substituted with electronic data processing, automatic cost function determination, and algorithmic parameter optimization.
2Adaptability or versatility
If multiple output values are used to represent system quality, then the comprehensiveness of system evaluation is improved, but the interpretability and direct use as cost functions becomes difficult
Solution Approach 1:
The patent introduces an intermediary processing layer that transforms multiple system output values into a unified cost function. The machine learning model acts as a mediator that learns the relationship between multiple outputs and system quality, automatically determining weighting and aggregation to create a single interpretable cost function from multiple evaluation dimensions.
Solution Approach 2:
The system dynamically determines parameter weightings and transformations based on learned relationships. The cost function parameters are not fixed but are automatically adjusted through the learning process to optimally represent system quality across multiple output dimensions, making the cost function both comprehensive and interpretable.
3Adaptability or versatility
If cost functions are adapted to specific customer requirements, then the relevance and applicability of the optimization is improved, but the complexity of the determination process increases
Solution Approach 1:
The cost function determination process is made dynamic and adaptive through machine learning. Instead of requiring manual redesign for each customer requirement, the system dynamically learns and adapts cost functions based on input data that reflects customer-specific requirements. The model can be retrained or fine-tuned with new data to adapt to different applications.
Solution Approach 2:
The patent creates a universal framework that can handle diverse customer requirements through a single automated system. The machine learning approach provides a multi-functional platform that can determine cost functions for various technical systems and application scenarios without requiring separate manual processes for each case.
4Productivity
If automated methods are used to determine system parameters, then the productivity and efficiency are improved, but the reliability and quality of determination may be compromised without expert knowledge
Solution Approach 1:
The system incorporates feedback mechanisms where the automated parameter determination results are evaluated and used to improve future determinations. The machine learning model learns from the outcomes and system behavior, continuously refining its cost function determination and parameter optimization to maintain high reliability while operating autonomously.
Data Source
AI summary
A method for determining technical system parameters using a cost function. The technical system has system parameter-adjustable components. When system parameters are set, the technical system generates component output values. The method includesdetermining a definition space in which the cost function lies,determining random system parameters in the definition space,applying the random system parameters to the technical system and determining the output values,technical system modeling by training a statistical analysis method,generating technical system rules using the system parameters and the output values,generating probability functions using the rules, each indicating the probability which satisfy the rules by any cost function,combining probability functions to determine the cost function by maximizing overall probability of all rules,optimizing the system parameters given the cost function, andoutputting the optimized system parameters for adjusting the technical system components.