Neural Network Techniques for Temperature Sensor Calibration

In modern measurement science, ensuring accurate temperature readings across diverse environments is critical for applications from industrial processing to environmental monitoring. Neural network techniques have emerged as a powerful tool for calibrating temperature sensors by modelling and compensating for intrinsic nonlinearity, sensor ageing and environmental drift. Unlike traditional polynomial or lookup-table methods, neural networks can approximate complex sensor transfer functions and adapt to varying operating conditions, often requiring fewer calibration points and offering improved generalisation. Feedforward architectures, such as multilayer perceptrons, are widely used to capture static sensor characteristics, while recurrent and convolutional variants enable dynamic calibration in the presence of transient thermal responses and spatially distributed measurement arrays. Recent advances in lightweight network design and hardware acceleration have facilitated deployment on microcontroller platforms, allowing real-time inference with minimal energy and memory budgets. These techniques enhance the robustness of temperature measurement systems by integrating error compensation for manufacturing tolerances, cross-sensitivity and long-term drift into a single adaptive model. By combining data-driven networks with classical sensor physics, hybrid calibration frameworks have also been developed to ensure physical interpretability and reliability under safety-critical conditions.

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Recent studies have demonstrated the implementation of lightweight multilayer perceptrons on low-power microcontrollers to directly calibrate thermistor and resistance-temperature detector (RTD) outputs. Customised network architectures with one or two hidden layers have achieved calibration errors below 0.1 °C over wide temperature ranges, while maintaining inference times compatible with real-time control loops. Another strand of research has applied convolutional neural networks to arrays of temperature sensors, jointly compensating for spatial non-uniformity and environmental influences such as ambient humidity. These networks exploit local correlations among sensor channels to enhance calibration accuracy without increasing the number of calibration points. Additionally, recurrent neural network approaches—particularly models based on long short-term memory cells—have been used to model the dynamic behaviour of sensor systems under sudden temperature changes, reducing transient overshoot errors and improving stability during rapid heating or cooling cycles. Emerging frameworks combine data-driven neural calibration with physics-based constraints, yielding hybrid models that maintain physical interpretability while benefiting from the flexibility of deep learning. These developments point to a new generation of calibration strategies that balance precision, computational efficiency and adaptability across varied industrial and environmental applications.

Neural Network Techniques for Temperature Sensor Calibration publication trend

The graph below shows the total number of articles in neural network techniques for temperature sensor calibration across all publications each year (not limited to Nature Index journals).

Technical terms

Calibration: the process of adjusting sensor output to match a standard or reference measurement.

Neural network: a computational model composed of interconnected nodes (neurons) that can learn complex nonlinear relationships from data.

Multilayer perceptron (MLP): a feedforward neural network with one or more hidden layers, commonly used for static function approximation.

Convolutional neural network (CNN): a network architecture that applies convolutional filters to extract spatial or local features, useful for sensor arrays.

Recurrent neural network (RNN): a neural network designed to handle sequential data by maintaining internal memory of previous inputs.

Long short-term memory (LSTM): an RNN variant that captures long-range dependencies in time-series data, improving dynamic calibration.

Drift compensation: techniques to correct gradual changes in sensor output due to ageing or environmental factors.

References

1. Lookup Table Optimization for Sensor Linearization in Small Embedded Systems. Journal of Sensor Technology (2012).
2. Real-time Neural Networks Implementation Proposal for Microcontrollers. Electronics (2020).