Neural Network Noise Modeling via Squeezed Space Regression
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Solution Overview
Problem
Neural networks face challenges with overfitting and underfitting due to small or large datasets, which can lead to poor performance, especially when dealing with noisy or sparse sampling in the input space, and existing noise addition methods like Gaussian noise lack precision in adjusting noise levels effectively.
Innovation Solution
The method involves modeling environmental, internal system, and user noise using polynomial regressions to generate upper and lower bound functions, which are combined to create a squeezed noise function. This function is used to detect anomalies and generate sample values, allowing for the calculation of an average noise index that alters the training data to emulate a noisy environment, thereby improving neural network training by distinguishing normal data from background noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If Gaussian noise is added to input variables during training, then the input space becomes smoother and easier to learn, but the mapping function becomes too challenging to learn when too much noise is added
Solution Approach 1:
The patent transforms the fixed Gaussian noise parameter into a dynamic, data-driven noise index calculated through polynomial regression analysis. The noise level adapts based on detected anomalies and environmental factors, changing parameters from static to dynamic to resolve the contradiction between ease of learning and learning accuracy
Solution Approach 2:
The system implements feedback by detecting anomalies in training data, calculating a noise index based on these detections, and using this index to modulate the amount of noise added during training. This closed-loop feedback mechanism adjusts noise levels dynamically to maintain optimal learning conditions
2Measurement precision
If polynomial regression is used to model noise with upper and lower bound functions, then precision in adjusting noise levels is improved, but device complexity increases
Solution Approach 1:
The patent segments the noise modeling process into distinct polynomial regression components (upper bound function, lower bound function, and squeezed function). Each component handles a specific aspect of noise characterization, dividing the complex task into manageable segments that collectively achieve precise noise level adjustment
3Measurement precision
If anomalies are detected and sample values are generated from them, then the ability to distinguish normal data from background noise is improved, but loss of time increases due to additional processing
Solution Approach 1:
The system performs preliminary anomaly detection and noise index calculation during the offline training phase before actual deployment. This preliminary action prepares the noise characteristics in advance, reducing real-time processing requirements and minimizing time loss during operational use
Data Source
AI summary
An approach for altering alter training data and training process associated with a neural network to emulate environmental noise and operational instrument error by using the concepts of shots to sample within a squeezed space model, wherein shots are an uncertainty index that is the average of all shots from a sampling, is disclosed. The approach leverages a squeeze theorem to create a squeezed space model based on the regression of the upper and lower bound associated with the environmental noise and instrument error. The approach calculates an average noise index based on the squeezed space model, wherein the index is used to alter the training data and process.