Fuzzylitex

Fuzzylitex is a C++ library for Fuzzy Inference System Modeling and Identification. It is an extensions to the fuzzylite project that will be hopefully integrated into the official fuzzylite project.

How to Cite

Please, cite this project as the following paper (BibTeX format):

@MISC{Fuzzylitex,
	author = {Marco Guazzone},
	title = {{Fuzzylitex}: a {C++} library for Fuzzy Inference System Modeling and Identification},
	year = {2015},
	doi = {10.5281/zenodo.592585}
	url = {https://doi.org/10.5281/zenodo.592585},
}

In addition, you can cite the following papers where Fuzzylitex has been used to carry out the experimental evaluation (BibTeX format):

@JOURNAL{Prometheus,
    author = {Cosimo Anglano and Massimo Canonico and Marco Guazzone},
    title = {{Prometheus}: a flexible toolkit for the experimentation with virtualized infrastructures},
    journal = {Concurrency and Computation: Practice and Experience},
    year = {2017},
    doi = {10.1002/cpe.4400},
    url = {http://dx.doi.org/10.1002/cpe.4400},
    keywords = {toolkit, resource management, experimental evaluation, physical testbed, virtualization},
    note = {Accepted for publication},
}

ANN Extension

This extension provides basic functionalities for Artificial Neural Networks (ANN). This extension is under the fl::ann namespace.

Features

• Multilayer perceptron
• Learning algorithms:
  • Gradient descent with momentum backpropagation learning algorithm (e.g., see [Hagan1996,Rojas1996]), with both offline and online learning mode.
• Weight randomizers and initializers:
  • Constant value weight weight randomizer
  • Hard range weight randomizer
  • Nguyen-Widrow's weight randomizer
  • Gaussian weight randomizer

Limitations

• Only the multilayer perceptron architecture is available
• Only few variants of the backpropagation algorithm are available

Requirements

• Boost C++ library and in particular:
  • Boost.CurrentFunction
  • Boost.MPL
  • Boost.Noncopyable
  • Boost.Random (unless a C++11 compiler is used)
  • Boost.Utility (unless a C++11 compiler is used)
  • Boost.TypeErasure
  • Boost.TypeTraits (unless a C++11 compiler is used)

ANFIS Extension

This extension provides both the Adaptive Neuro Fuzzy Inference System (ANFIS) and the related Coactive ANFIS (CANFIS) model to support multiple outputs. For a complete discussion on such models see, for instance, [Jang1993,Jang1997]. This extension is under the fl::anfis namespace.

Features

• Multiple outputs
• Learning algorithms:
  • Gradient descent backpropagation learning algorithm with adaptive learning rate based on the step-size (see [Jang1993,Jang1997]), with both offline and online learning mode.
  • Gradient descent with momentum backpropagation learning algorithm (see [Hagan1996]), with both offline and online learning mode.
  • Combined gradient descent and least-squares estimation hybrid learning algorithm with adaptive learning rate based on the step-size (see [Jang1993,Jang1997]), with both offline and online learning mode.
• Takagi-Sugeno-Kang and Tsukamoto fuzzy inference system

Limitations

• User defined terms are not supported
• Terms with height other than one are not supported
• The only hedge currently supported in the rule antecedent is the NOT hedge.
• The combined gradient descent and least-squares estimation hybrid learning algorithm currently support the Takagi-Sugeno-Kang fuzzy inference system only.

Requirements

Unless a C++11 compiler is used, the following libraries are needed:

• Boost C++ library and in particular:
  • Boost.CurrentFunction
  • Boost.Utility
  • Boost.TypeTraits

References

1. [Hagan1996] M.T. Hagan et al., "Neural Network Design," Boston, MA: PWS Publishing, 1996.
2. [Jang1993] J.-S.R. Jang, "ANFIS: Adaptive-Network-based Fuzzy Inference Systems," IEEE Transactions on Systems, Man, and Cybernetics, 23:3(665-685), 1993.
3. [Jang1997] J.-S.R. Jang et al., "Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence," Prentice-Hall, Inc., 1997.
4. [Rojas1996] R. Rojas, "Neural Networks: A Sistematic Introduction," Springer, 1996.