Fuzzy Logic Toolbox

Modeling Using Fuzzy Logic

The Fuzzy Logic Toolbox lets you apply neurofuzzy and clustering techniques to model and classify system behavior.

Adaptive Neurofuzzy Inference

Using the Neuro-Fuzzy Design app, you can shape membership functions by training them with input/output data rather than specifying them manually. The toolbox uses a back propagation algorithm alone or in combination with a least squares method, enabling your fuzzy systems to learn from the data.

The Neuro-Fuzzy Design app constructs and tunes a FIS based on the data being modeled.
The Neuro-Fuzzy Design app constructs and tunes a FIS based on the data being modeled.

Fuzzy Clustering

The Fuzzy Logic Toolbox provides support for fuzzy C-means and subtractive clustering, modeling techniques for data classification and modeling.

The Fuzzy Clustering tool uses numerical data to develop classification and system modeling algorithms.
The Fuzzy Clustering tool uses numerical data to develop classification and system modeling algorithms.
Next: Simulating and Deploying Fuzzy Inference Systems

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Machine Learning with MATLAB

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