Adaptive Neural Network Switching Across Changing Settings
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Solution Overview
Problem
Conventional neural networks face accuracy loss and catastrophic memory issues when operating in varying environments, leading to suboptimal performance as they are trained with increasingly large datasets to cover multiple settings.
Innovation Solution
A system that collects training data for specific settings, associates characteristics with neural network coefficients and structures, and stores them in a database management system, allowing for adaptive reconfiguration of neural networks based on changing settings by retrieving and instantiating the appropriate coefficients and structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If training data from multiple different settings is combined to train a single neural network, then the neural network can operate in various environments, but accuracy is lost and catastrophic memory loss occurs
Solution Approach 1:
The patent segments the training process by collecting training data separately for each specific setting (environment, condition, or situation) and training individual neural networks or coefficient sets for each setting. This segmentation prevents the catastrophic memory loss that occurs when all settings are combined into a single training dataset, while maintaining high accuracy for each specific setting through dedicated training data.
Solution Approach 2:
The patent implements dynamic reconfiguration of neural networks based on detected settings. The system monitors current operating conditions and automatically switches between different trained neural networks or coefficient sets corresponding to different settings. This dynamic adaptation allows the system to maintain high accuracy for the current setting while retaining the capability to operate across multiple different environments.
2Adaptability or versatility
If training data from multiple different settings is combined to train a single neural network, then the neural network can operate in various environments, but catastrophic memory loss occurs
Solution Approach 1:
The patent segments the neural network system into multiple stable, pre-trained components, each optimized for a specific setting. By maintaining separate trained models or coefficient sets for different settings rather than combining them, the system preserves the structural stability and performance characteristics of each individual network while achieving overall versatility through selective reconfiguration.
Solution Approach 2:
The patent creates copies of neural network structures, each trained on setting-specific data. Instead of modifying a single network to handle all settings (which causes instability), the system maintains multiple copies tailored to different settings and selects the appropriate copy based on current conditions, thereby preserving the stability of each network structure.
3Adaptability or versatility
If ever increasing quantities of training data are used to train neural networks for multiple settings, then coverage of different environments increases, but accuracy is lost
Solution Approach 1:
The patent applies local quality by training each neural network or coefficient set on setting-specific training data, creating locally optimized models for each environment, condition, or situation. This approach ensures high classification accuracy for each specific setting by using targeted training data, while the collection of locally optimized models provides comprehensive environmental coverage through selective deployment.
Data Source
AI summary
Methods and systems that allow neural network systems to maintain or increase operational accuracy while being able to operate in various settings that may include predicting information. A set of training data is collected over each of at least two different settings. Each setting has a set of characteristics. Examples of setting characteristic types can be time, geographical location, and/or weather condition. Each set of training data is used to train a neural network resulting in a set of coefficients. For each setting, the setting characteristics are associated with the corresponding neural network having the resulting coefficients and neural network structure. A neural network, having the coefficients and neural network structure resulted after training using the training data collected over a setting, would yield optimal results when operated in/under the setting. A database management system can store information relating to the setting characteristics, neural network coefficients, and/or neural network structures.