Machine Learning Circuit for Nonlinear Measurement Correction
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Solution Overview
Problem
Conventional methods for correcting the output power of electronic measurement systems fail to achieve optimal results due to non-linear relationships between actual and set output powers, exacerbated by hardware and environmental factors like temperature and humidity.
Innovation Solution
A high-precision measurement system utilizing a data collection circuit, machine learning circuit, and output circuit, which employs a machine learning model to generate correction parameters based on collected data, creating a lookup table to accurately adjust output data to match set values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the point-slope method is used to correct output power, then the correction process is simple, but the measurement precision deteriorates due to non-linear relationship
Solution Approach 1:
The patent transforms the correction approach from linear parameter adjustment (point-slope method) to non-linear parameter transformation through machine learning models. The system collects calibration data across multiple operating points, trains ML models to learn the non-linear relationship between actual and set output powers, and applies these learned parameters for accurate correction, thereby resolving the contradiction between simplicity and precision.
Solution Approach 2:
The patent replaces the conventional mathematical correction mechanism (point-slope method) with a machine learning-based correction system. The ML models automatically learn and adapt to the non-linear characteristics of the measurement system, substituting manual linear correction with intelligent non-linear correction that achieves higher precision while maintaining operational simplicity through automated model inference.
2Ease of operation
If conventional linear correction is applied, then the system operation is straightforward, but the reliability deteriorates under varying environmental conditions
Solution Approach 1:
The patent introduces dynamic adaptation into the correction system by training machine learning models on calibration data collected under various environmental conditions (temperature, humidity). The models learn to dynamically adjust correction parameters based on the relationship between environmental factors and measurement errors, enabling the system to maintain high reliability across varying conditions while keeping operation straightforward through automated environmental compensation.
Solution Approach 2:
The system implements feedback mechanisms where calibration data from actual measurements is used to train and refine the machine learning models. The models continuously learn from the feedback between set output power, actual output power, and environmental conditions, improving the reliability of corrections under varying environmental conditions while maintaining ease of operation through automated feedback-driven adaptation.
Data Source
AI summary
A high-precision measurement system is disclosed. The high-precision measurement system includes a data collection circuit, a machine learning circuit, and an output circuit. The data collection circuit is configured to obtain several first output data corresponding to several first setting data. The machine learning circuit is configured to create a machine learning model according to the several first setting data, the several first output data, and several first correction parameters between the several first setting data and the several first output data, and the machine learning circuit is configured to generate a second correction parameter corresponding to a second setting data according to the machine learning model. The output circuit is configured to correct a second output data corresponding to the second setting data to generate a corrected output data according to the second correction parameter.