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Published on: December 15, 2023
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Proportional-Integral-Observer-Based Fusion Estimation for Artificial Neural Networks: Implementing a One-Bit
Summary
This study introduces a novel one-bit encoding mechanism (OBEM) for artificial neural networks (ANNs) with multiple sensors. The proposed proportional-integral-observer (PIO)-based fusion estimation effectively handles bandwidth constraints and unknown-but-bounded noises (UBBNs).
Area of Science:
- Control Systems Engineering
- Artificial Intelligence
- Signal Processing
Background:
- Artificial neural networks (ANNs) with multiple sensors face challenges with bandwidth constraints and unknown-but-bounded noises (UBBNs).
- Efficient information communication is crucial for sensor networks, necessitating advanced data encoding techniques.
- Existing estimation methods may not adequately address data distortion introduced by encoding mechanisms.
Purpose of the Study:
- To develop a proportional-integral-observer (PIO)-based fusion estimation method for ANNs with multiple sensors.
- To address bandwidth constraints and unknown-but-bounded noises (UBBNs) in sensor data.
- To propose a one-bit encoding mechanism (OBEM) for efficient scalar data transmission.
Main Methods:
- Devised local PIO-based set-membership estimators for each sensor node.
- Incorporated the one-bit encoding mechanism (OBEM) to handle data distortion.
- Introduced an ellipsoid-based fusion rule for enhanced global estimation performance.
- Utilized set theory and optimization methods for performance analysis and parameter determination.
Main Results:
- Established sufficient conditions for the existence and effectiveness of the PIO-based set-membership estimator.
- Demonstrated improved fusion estimation performance through the ellipsoid-based fusion rule.
- Validated the proposed estimation algorithm's effectiveness and advantages via a simulation example.
Conclusions:
- The proposed PIO-based fusion estimation algorithm effectively addresses bandwidth constraints and UBBNs in ANNs.
- The one-bit encoding mechanism (OBEM) enables efficient data communication with manageable distortion.
- The ellipsoid-based fusion rule enhances global estimation accuracy for multi-sensor ANNs.
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