Non-linear Prediction Model Selection for Physical Systems
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Solution Overview
Problem
Existing techniques for identifying unknown samples using chemical sensors are limited by the assumption of a linear input-output relation, leading to accuracy issues when the relation is non-linear.
Innovation Solution
An information processing apparatus that acquires use environment information, selects a prediction model from a storage unit based on section information matching the environment, and performs prediction using the selected model to handle non-linear input-output relations in physical systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a linear prediction model is used for physical systems, then the model structure is simple, but the prediction accuracy deteriorates when the input-output relation is non-linear
Solution Approach 1:
The patent divides the non-linear prediction problem into multiple linear sub-problems by segmenting the input space into different regions. Each region has its own linear prediction model, allowing the system to maintain simple model structures while accurately capturing non-linear relationships through piecewise linear approximation.
Solution Approach 2:
The patent implements a dynamic model selection mechanism that automatically chooses the appropriate linear model based on the current input characteristics. This dynamic adaptation allows the system to switch between different linear models to match the local linear behavior of the non-linear system, resolving the contradiction between model simplicity and accuracy.
2Measurement precision
If multiple prediction models are stored for different sections, then the prediction accuracy for non-linear systems improves, but the device complexity increases
Solution Approach 1:
The patent performs preliminary segmentation of the input space and pre-stores linear models for each segment. This preliminary action allows the system to have multiple models ready for different operating conditions, improving prediction accuracy while managing complexity through organized model storage and efficient selection based on input characteristics.
Data Source
AI summary
An information processing apparatus (20) includes a use environment information acquisition unit (210), a model selection unit (220), and a prediction unit (230). The use environment information acquisition unit (210) acquires use environment information indicating a use environment of a physical system having input-output. The model selection unit (220) selects, from a storage unit storing a plurality of prediction models of the physical system in association with section information indicating a section based on the use environment, a prediction model being associated with section information of a section matching the use environment indicated by the use environment information. The prediction unit (230) performs prediction based on output of the physical system by use of the selected prediction model.