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Fuzzy Logic Toolbox 2.2.12
Product Description
- Introduction and Key Features
- Working with the Fuzzy Logic Toolbox
- Building a Fuzzy Inference System
- Modeling Using Fuzzy Logic
- Simulating and Deploying Fuzzy Inference Systems
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 Adaptive Neuro-Fuzzy Inference System (ANFIS) Editor, 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 ANFIS Editor 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 GUI uses numerical data to develop classification and system modeling algorithms.
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