Adversarial Training for Noisy Time Series Classification
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Solution Overview
Problem
Machine learning systems processing sensor signals are often degraded by noise, particularly in time series data, leading to reduced predictive accuracy, and existing methods fail to effectively enhance robustness against noise.
Innovation Solution
A computer-implemented machine learning system is trained using adversarial training methods by introducing a worst possible noise signal, calculated based on expected noise patterns, to adapt its parameters and improve robustness against noise interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If adversarial training with worst possible noise signal is applied, then robustness to noise is improved, but training complexity and computational resources increase
Solution Approach 1:
The method applies preliminary action by pre-calculating the worst possible noise signal based on expected noise patterns before actual training. This allows the system to prepare adversarial examples in advance, reducing the need for complex real-time noise generation during training while maintaining robustness improvement.
Solution Approach 2:
The method changes parameters by transforming the training approach from standard clean data training to adversarial training with controlled noise parameters. By adjusting the noise signal parameters (magnitude, frequency, type) to create worst-case scenarios, the system improves robustness without requiring fundamental changes to the neural network architecture.
2Productivity
If standard training methods are used, then training speed is maintained, but predictive accuracy degrades under noise interference
Solution Approach 1:
The method converts harm into benefit by taking the harmful noise signal and transforming it into a beneficial training element. By deliberately introducing worst possible noise patterns during training, the system learns to withstand and accurately process noisy sensor data, thereby improving predictive accuracy under noise interference while maintaining training throughput.
3Reliability
If noise robustness is enhanced through traditional methods, then reliability improves, but the system becomes more sensitive to adversarial examples
Solution Approach 1:
The method applies preliminary anti-action by proactively defending against adversarial examples through adversarial training. By exposing the neural network to worst possible noise signals and adversarial perturbations during training, the system develops immunity to these harmful inputs before deployment, simultaneously improving noise robustness and reducing sensitivity to adversarial attacks.
Data Source
AI summary
A computer-implemented machine learning system configured to ascertain an output signal based on a time series of input signals of a technical system. The output signal characterizes a classification and/or a regression result of at least one first operating state and/or at least one first operating variable of the technical system. The training of the machine learning system includes: ascertaining a first training time series of input signals from a plurality of training time series and a desired training output signal which corresponds to the first training time series; ascertaining a worst possible training time series which characterizes an overlap of the first training time series with an ascertained first noise signal; ascertaining a training output signal based on the worst possible training time series using the machine learning system; and adapting at least one parameter of the machine learning system according to a gradient of a loss value.