Electronic System Parameter Adjustment Under Measurement Error
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing adjustment methods for electronic systems are prone to making erroneous decisions due to measurement errors, leading to suboptimal adjustments, especially when dealing with multi-dimensional spaces, as they rely on a single best target function value, and require extensive evaluation of all possible parameter values, which is time-consuming and inefficient.
Innovation Solution
Classifying target function values into two categories based on a threshold criterion, allowing the adjustment method to consider multiple values closer to the target, thereby reducing the impact of measurement errors and defining a modified initial range that focuses on regions near optimal limit values, ensuring a more precise and efficient search in the adjustment space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single best target function value is used to define the new initial range, then the adjustment method is simple and quick, but measurement errors cause erroneous decisions and suboptimal adjustments
Solution Approach 1:
The patent changes the parameter selection criterion from selecting a single best target function value to selecting multiple target function values that satisfy a threshold criterion. This parameter change in the selection criteria allows the system to tolerate measurement errors while maintaining efficient convergence, resolving the contradiction between speed and reliability.
2Reliability
If all possible parameter values in the entire adjustment space are systematically evaluated, then error tolerance increases, but the time required for acquiring and evaluating physical quantities becomes unacceptably long
Solution Approach 1:
The patent applies local quality by focusing the search only on regions of the adjustment space that satisfy the threshold criterion, rather than evaluating all possible parameter values uniformly. This localized approach maintains high error tolerance by considering multiple candidate regions while significantly reducing the total evaluation time compared to exhaustive search.
Solution Approach 2:
The patent performs partial action by evaluating only those parameter values that meet the threshold criterion rather than all possible values. This partial evaluation approach provides sufficient error tolerance for practical applications while avoiding the excessive time cost of complete enumeration.
3Productivity
If the initial range is reduced by half at each iteration using binary search, then the convergence is fast, but measurement errors lead to incorrect decisions about range assignment
Solution Approach 1:
The patent applies beforehand cushioning by pre-defining a threshold criterion that cushions against measurement errors. By establishing this threshold before iterations begin, the system creates a safety margin that prevents erroneous decisions even when measurement precision is limited, allowing fast convergence without sacrificing decision accuracy.
Data Source
AI summary
A method for adjusting an electronic system is provided in which it is possible to predefine the n parameters of the system which correspond to an n-dimensional adjustment space, wherein at the start of the adjustment each parameter has predefined for it two limit values that delimit an appropriate initial range in the n-dimensional adjustment space, and wherein the following steps are repeated until a termination condition is achieved: evaluating a target function that quantifies the achievement of an adjustment target for the limit values that delimit the initial range, wherein the evaluation includes the measurement and/or evaluation of at least one physical quantity of the system that is a function of the specific parameter or its limit value, and wherein appropriate target function values associated with the limit values are obtained, defining a modified, in particular reduced, initial range for a subsequent iteration as a function of the target function values obtained. According to the invention, the target function values are assigned to two different classes, wherein all target function values that correspond to a target criterion are assigned to a first class, and wherein all target values that do not correspond to the target criterion are assigned to a second class, and in that defining of the modified initial range for the subsequent iteration takes place as a function of the target function values assigned to the first class.