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Related Experiment Video

Updated: Jun 2, 2026

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

Published on: March 10, 2011

Moving-horizon state estimation for nonlinear systems using neural networks.

Angelo Alessandri¹, Marco Baglietto, Giorgio Battistelli

¹Department of Production Engineering, Thermoenergetics,and Mathematical Models, University of Genoa, Genova, Italy. alessandri@diptem.unige.it

IEEE Transactions on Neural Networks

|May 10, 2011

Summary

This summary is machine-generated.

This study introduces a novel moving-horizon (MH) state estimation method for nonlinear systems with bounded noise. The approach uses neural networks to reduce computational load, offering an effective alternative to existing techniques.

Related Experiment Videos

Last Updated: Jun 2, 2026

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

Published on: March 10, 2011

Area of Science:

Control Engineering
Nonlinear System Analysis
Computational Intelligence

Background:

Moving-horizon (MH) state estimation is crucial for nonlinear discrete-time systems.
Systems are often affected by bounded noises in both system and measurement equations.
Minimizing a sliding-window least-squares cost function is a common but computationally intensive approach.

Purpose of the Study:

To develop an efficient MH state estimation technique for nonlinear discrete-time systems with bounded noises.
To reduce the online computational effort associated with MH estimation.
To leverage parameterized approximating functions for improved design.

Main Methods:

Employing nonlinear parameterized approximating functions, specifically feedforward neural networks.
Implementing a sliding-window least-squares cost function minimization.
Allowing for suboptimal solutions by permitting a certain error in cost function minimization.
Utilizing offline optimization of neural network parameters.

Main Results:

The proposed MH estimation scheme significantly reduces online computational requirements.
Simulation results demonstrate the effectiveness of the neural network-based approach.
The method shows comparable or superior performance against other estimation techniques.

Conclusions:

The developed MH state estimation method effectively handles nonlinear systems with bounded noise.
The use of offline-optimized neural networks provides a computationally efficient solution.
This approach offers a promising alternative for practical state estimation problems.