Slides for Fuzzy Sets, Ch. 2 of Neuro-Fuzzy and Soft Computing
Slides for Fuzzy Sets, Ch. 2 of Neuro-Fuzzy and Soft Computing
Slides for Fuzzy Sets, Ch. 2 of Neuro-Fuzzy and Soft Computing
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<strong>Neuro</strong>-<strong>Fuzzy</strong> <strong>and</strong> S<strong>of</strong>t <strong>Computing</strong>: <strong>Fuzzy</strong> <strong>Sets</strong><br />
Least-Square Estimators<br />
We have m outputs & n fitting parameters to find (or m<br />
equations & n unknown variables)<br />
Usually, m is greater than n, since the model is just an<br />
approximation <strong>of</strong> the target system <strong>and</strong> the data<br />
observed might be corrupted. There<strong>for</strong>e, an exact<br />
solution is not always possible.<br />
To overcome this inherent conceptual problem, an error<br />
vector e is added to compensate<br />
A θ + e = y<br />
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