Single Layer Perceptron Neural Network

Single Layer Perceptron Neural Network - Binary Classification Example


Updated Mon, 27 Apr 2020 20:00:22 +0000

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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'

Cite As

Shujaat Khan (2023). Single Layer Perceptron Neural Network (, MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2020a
Compatible with any release
Platform Compatibility
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- Example