Dual-Model Calculation Switching for Unlearned Environments
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Solution Overview
Problem
Conventional AI models require relearning when environmental conditions change, leading to unintended output results and inefficiencies in calculation time and performance, especially in unlearned environments.
Innovation Solution
A calculation device with dual calculation units: one using a model with learned parameters and another with fixed parameters, allowing flexible switching between them based on environmental conditions, enabling continuous operation during learning and improving execution time and output results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a learned model is used to improve processing capability and output precision, then calculation precision and operation smoothness are improved, but the model cannot be used until learning is completed and relearning is required when environmental conditions change
Solution Approach 1:
The patent implements dynamic switching between the learned model and the fixed model based on environmental conditions. The determination unit detects whether the current environment matches the learning environment, and the switching unit dynamically selects which model to use, allowing the system to adapt its calculation approach in real-time rather than being static
Solution Approach 2:
The patent changes the operational parameters of the model by introducing a determination mechanism that evaluates environmental conditions. When the environment matches the learning environment, the learned model parameters are used; when they differ, the fixed model parameters are used instead, effectively changing the system's operational state based on external conditions
2Productivity
If a learned model is used to improve calculation speed and output results, then execution efficiency is improved, but the model produces unintended results when environmental conditions change
Solution Approach 1:
The determination unit provides feedback about environmental conditions to the switching unit. This feedback mechanism allows the system to monitor whether the current environment matches the learning environment and adjust model selection accordingly, ensuring reliable output results across different conditions
Solution Approach 2:
The determination unit acts as an intermediary between the environmental conditions and the model selection process. It evaluates whether the current environment matches the learning environment and mediates the choice between using the learned model or the fixed model, ensuring appropriate model selection
3Stability of the object's composition
If a fixed model is used to ensure stable operation, then system stability is maintained, but the model cannot adapt to new patterns or environmental changes
Solution Approach 1:
The patent creates a universal system that can perform multiple functions by combining both the learned model and the fixed model. The switching unit enables the system to universally handle both stable, predictable environments (using the fixed model) and new, changing environments (using the learned model), making the system adaptable to various conditions
Data Source
AI summary
A calculation device, a calculation method, and a storage medium are provided. The present invention is provided with: a first calculation unit which, with respect to input data, performs calculations relating to predetermined processing by using a first model in which a corresponding relationship between input data and output data changes by performing machine learning that uses learning data, and outputs a first output; a second calculation unit which, with respect to input data, performs calculations relating to predetermined processing using a second model for which the correspondence relationship between input data and output data is fixed, and outputs a second output; and a comparison unit which, on the basis of a comparison result obtained by comparing the first output and the second output with a prescribed determination standard, outputs the first output, the second output, or a third output that is a combination of the first and second outputs.