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Related Experiment Video

Updated: Sep 27, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Guaranteed approximation error estimation of neural networks and model modification.

Yejiang Yang¹, Tao Wang¹, Jefferson P Woolard²

¹National Rail Transit Electrification and Automation Engineering Technique Research Center, School of Electrical Engineering, Southwest Jiaotong University, Chengdu, 610000, China.

Neural Networks : the Official Journal of the International Neural Network Society

|April 8, 2022

Summary

This study introduces guaranteed error estimation for neural networks, providing worst-case approximation error bounds. This method enhances system modeling and neural network compression by ensuring reliable performance.

Keywords:

Approximation error estimation Feedforward neural network Lipschitz constant Neural network compression Reachability

Related Experiment Videos

Last Updated: Sep 27, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Area of Science:

Computer Science
Artificial Intelligence
Control Theory

Background:

Approximation error is crucial for neural network (NN) validation and verification.
Current methods lack guaranteed error bounds for complex systems.

Purpose of the Study:

To propose a novel concept of guaranteed error estimation for feedforward neural networks.
To develop efficient methods for computing worst-case approximation errors.
To apply these methods to assured system modeling and NN compression.

Main Methods:

Introduced the concept of guaranteed error estimation for feedforward neural networks.
Developed two approaches: Lipschitz constant analysis and set-valued reachability analysis.
Proposed an optimization for parameter estimation based on the error framework.

Main Results:

Efficient computation of upper-bounds for approximation errors.
Demonstrated effectiveness through robotic arm and NN compression examples.
Established a framework for assured system modeling and NN compression.

Conclusions:

The proposed guaranteed error estimation framework provides reliable worst-case error bounds for neural networks.
The developed methods are effective for assured system modeling and neural network compression.
This approach advances the verification and validation of neural network systems.