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RFIS: Regression-based Fuzzy Inference System

version 1.0.5 (158 KB) by Krzysztof Wiktorowicz
RFIS is a novel simple fuzzy inference system without explicitly defined fuzzy rules based on linear and nonlinear regressions.

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Updated 16 May 2022

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This project concerns training fuzzy systems on the basis of linear and nonlinear regressions. These systems use Gaussian fuzzy sets for the inputs and linearly and nonlinearly parameterized system functions to obtain the output. The linear regression is realized by the ridge regression and the nonlinear regression by Levenberg-Marquardt algorithm. The input fuzzy sets are determined by a multi-objective genetic algorithm with a feature selection method. In the case of linearly parameterized system functions, the following methods are considered: F-test and a regression tree. In the case of nonlinearly parameterized system functions, terms from the so-called term matrix are coded in an individual and they are selected by using a genetic algorithm. The multi-criteria objective functions enable the selection of models from the Pareto fronts taking into account the compromise between model accuracy and its simplification.
The package contains an example of using the RFIS method to predict a time series based on the Box-Jenkins gas furnace data set. The data can be downloaded from: https://openmv.net/info/gas-furnace
Using this method, please cite as:
Wiktorowicz K., 'RFIS: regression-based fuzzy inference system', Neural Computing and Applications, 2022, DOI: 10.1007/s00521-022-07105-8
This folder contains:
- common files:
gauss.m
myridge.m
regmat.m
desmat.m
selectfromPareto.m
evalrfis.m
- simple example:
illustrative_example
illustrative_fun_nlm
- predicting a time-series for Box-Jenkins data:
boxjen.dat
boxjen_init.m
boxjen_objfun.m
boxjen_main.m
boxjen_optim.m
boxjen_fun_nlm.m
boxjen_objfun_nlm.m
boxjen_main_nlm.m
boxjen_optim_nlm.m
To see a simple example, run:
illustrative_example
To train a fuzzy model for the Box-Jenkins data, run:
boxjen_main.m
boxjen_optim.m
or
boxjen_main_nlm.m
boxjen_optim_nlm.m

Cite As

Krzysztof Wiktorowicz (2022). RFIS: Regression-based Fuzzy Inference System (https://www.mathworks.com/matlabcentral/fileexchange/95848-rfis-regression-based-fuzzy-inference-system), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2020a
Compatible with any release
Platform Compatibility
Windows macOS Linux
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