What is it about?
Forgetting Factor is a great tool in non-statonary systems, and help in different descriptions such as estimation and identifications techniques in line. Commonly, in brain sciences different techniques require the exponential forgetting factor to obtain a great convergence leves, as presented in the paper.
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Why is it important?
The results presented in the paper allows the brain signal professionals solve in line problems relationated with fenomena description considering the patient information previously capture.
Perspectives
In our studies require different cerebral signals relationated with different problems, Obtaining in equivalent form the stability conditions and break points that will be analyzed in spite off the limits stablished by the signal in study.
Jose de Jesus Medel
Computer Research Centre
Read the Original
This page is a summary of: n-Iterative Exponential Forgetting Factor for EEG Signals Parameter Estimation, Computational Intelligence and Neuroscience, January 2018, Hindawi Publishing Corporation,
DOI: 10.1155/2018/4613740.
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Resources
Identification
In this site observes the identification and interval estimation considering equivalent black-box model
Identification and internal estimation based on equivalent black-box model
Identification and internal estimation based on equivalent black-box model, allowing describing the stability conditions that the electrical signals are emitted by the brain.
Contributors
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