Single Layer Perceptron Neural Network

Single Layer Perceptron Neural Network - Binary Classification Example
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Mise à jour 27 avr. 2020

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- Define two distributions as two classes.
- Sample 1000 points from two distributions and define their class labels.
- Create a linear classification model. Initialize random weights and plot samples and classification boundary
- Optimize weights using stochastic gradient descent (LMS) learning algorithm for least mean squared error.
- Compare initial classification boundary with final (optimized) classification boundary
- Plot learning curve (MSE vs epochs)
- Plot sigmoid function and it's derivative with-respect to stimulus 'x'

Citation pour cette source

Shujaat Khan (2024). Single Layer Perceptron Neural Network (https://www.mathworks.com/matlabcentral/fileexchange/75238-single-layer-perceptron-neural-network), MATLAB Central File Exchange. Récupéré le .

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Créé avec R2020a
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Version Publié le Notes de version
1.0.1

- Example

1.0.0