Fuzzy Computing: Basic Concepts. Sample application (sources) - 46KSample application (binaries) - 27K (Note: The application's sources may be obtained also as part of AForge.NET Framework) Introduction: Fuzzy Computing Fuzzy Logic, the core of the Fuzzy Computing, was introduced by professor Lofti A. Zadeh in 1965, as an alternative approach to solve problems when the classical set theory and discrete mathematics, therefore the classical algorithms, are unappropriate or too complex to use. To address a certain class of problems which a human being can solve easly, an approach that do not relies on mathematical rigor and precision, but an approach fault tolerant that can handle partial truths.
The Fuzzy Computing can handle qualitative values instead of quantitative values. It can define the so called linguistic variables, instead of the classical numeric variables, and can perform computing with theses variables, using fuzzy rules, simulating in a certain way the human reasoning processes. Fuzzy Sets Linguistic Variables. Fuzzy Framework. Introduction In the following article, we briefly introduce Fuzzy Framework library which supports calculations based on fuzzy logic in .NET. In past, there have been a couple of similar projects like the one described in [3], but no one matched exactly my requirements: Simplicity - everyone can understand the code, extend it, and make use of it throughout his systems.
Support both of continuous and discrete sets. Support of arbitrary fuzzy sets as long as we can describe them by a group of polynomial functions. In the following text, we outline the basics of fuzzy logic and fuzzy set theory, focusing on how it differs from the standard, Boolean logic and from crisp sets. Fuzzy Sets 2.1 What’s the difference? Fuzzy systems become handy when someone intends to work with vague, ambiguous, imprecise, noisy, or missing information [7]. Figure 1 - Elements Apple and Pear belong to the set Fruits, whereas Carrot and Broccoli do not. Element x either is or is not a member of set Fruits. Examples. Framework. AForge.NET is an open source C# framework designed for developers and researchers in the fields of Computer Vision and Artificial Intelligence - image processing, neural networks, genetic algorithms, fuzzy logic, machine learning, robotics, etc.
The framework is comprised by the set of libraries and sample applications, which demonstrate their features: AForge.Imaging - library with image processing routines and filters;AForge.Vision - computer vision library;AForge.Video - set of libraries for video processing;AForge.Neuro - neural networks computation library;AForge.Genetic - evolution programming library;AForge.Fuzzy - fuzzy computations library;AForge.Robotics - library providing support of some robotics kits;AForge.MachineLearning - machine learning library;etc. The work on the framework's improvement is in constants progress, what means that new feature and namespaces are coming constantly. Accord.NET Framework | accord-net. Machine Learning Repository: Data Sets.