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 improve versatility, then the neural network can operate in more environments, but accuracy is lost and catastrophic memory issues occur
Solution Approach 1:
The patent segments the training data and neural network configurations by specific settings (environmental conditions, situations, etc.). Instead of using a single large training set covering all settings, the system creates separate training data sets for each setting and trains separate neural networks or uses separate coefficient sets for each setting. This segmentation prevents the catastrophic memory loss and accuracy degradation that occurs when combining diverse training data, while still enabling the system to operate across multiple settings by selecting the appropriate segmented model for each condition.
2Adaptability or versatility
If training data set size is increased to cover more settings, then more environments can be handled, but catastrophic memory loss occurs and the network ceases to operate
Solution Approach 1:
The training data sets are segmented by setting characteristics rather than using a single large combined data set. Each segmented training set is sized appropriately for its specific setting, avoiding the memory overload that occurs when all data is combined into one large set.
Solution Approach 2:
The system performs preliminary organization of training data into setting-specific segments before training begins. Neural networks or coefficient sets are pre-trained for each specific setting with appropriately sized training data, so that during operation the system can directly deploy the pre-trained model for the current setting without needing to process or manage a large combined data set in memory.
3Device complexity
If a single neural network is trained to handle all settings, then device complexity is reduced, but accuracy is compromised in specific environments
Solution Approach 1:
The system implements a dynamic model selection mechanism that adapts to the current setting by selecting the appropriate pre-trained neural network or coefficient set. Rather than using a single static network for all conditions, the system dynamically switches between multiple specialized models based on the detected setting characteristics, maintaining low operational complexity while achieving high accuracy for each specific environment.
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. 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, for example, the setting characteristics, neural network coefficients, and/or neural network structures.