Type-1 Fuzzy Logic Systems

Queries about the software can be made to "qilian@sipi.usc.edu".

Copyright (c) 2000 by the University of Southern California. All rights reserved.

This software is experimental in nature and is provided on an "as is" basis only. The University SPECIFICALLY DISCLAIMS ALL WARRANTIES INCLUDING, WITHOUT LIMITATION, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE.

This software may be used for non-commercial purposes only, so long as this copyright notice is reproduced 
with each such copy made.

The software in this folder focuses on the computations and designs of type-1 FLSs.

Each M-file is keyed into a chapter of the book Uncertain Rule-Based Fuzzy Logic Systems: Introduction 
and New Directions, by Jerry M. Mendel, and published by Prentice-Hall, 2000.


Description of M-files 


Here, we provide brief descriptions of the M-files that appear in this folder. For more help on any specific file, 
type "help (filename)" at MATLAB prompt.

I. Singleton Mamdani Type-1 FLS
     
     sfls_type1.m: Compute the output(s) of a singleton type-1 FLS when the antecedent membership 
functions are Gaussian. (Chapter 5)

     train_sfls_type1.m: Tune the parameters of a singleton type-1 FLS when the antecedent membership 
functions are Gaussian, using some inputÐoutput training data. (Chapter 5)

     svd_qr_sfls_type1.m: Rule-reduction of a singleton type-1 FLS when the antecedent membership 
functions are Gaussian, using some inputÐoutput training data. (Chapter 5)


II. Non-Singleton Mamdani Type-1 FLS

     nsfls_type1.m: Compute the output(s) of a non-singleton type-1 FLS when the antecedent membership 
functions are Gaussian and the input sets are Gaussian. (Chapter 6)

     train_nsfls_type1.m: Tune the parameters of a non-singleton type-1 FLS when the antecedent membership 
functions are Gaussian, and the input sets are Gaussian, using some inputÐoutput training data. (Chapter 6)

     svd_qr_nsfls_type1.m: Rule-reduction of a non-singleton type-1 FLS when the antecedent membership 
functions are Gaussian, and the input sets are Gaussian, using some inputÐoutput training data. (Chapter 6)


III. TSK FLS

     tsk_type1.m: Compute the output(s) of a type-1 TSK FLS (type-1 antece-dents and type-0 consequent) 
when the antecedent membership functions are Gaussian. (Chapter 13)
 
     train_tsk_type1.m: Tune the parameters of a type-1 TSK FLS (type-1 ante-cedents and type-0 consequent) 
when the antecedent membership functions are Gaussian, using some inputÐoutput training data. (Chapter 13)

