Title :
Design of interval type-2 fuzzy logic systems using prior knowledge via optimization algorithms
Author :
Tiechao Wang ; Jianqiang Yi
Author_Institution :
Inst. of Autom., Beijing, China
Abstract :
The paper presents the methods of integrating prior knowledge with a first-order Single-Input Single-Output (SISO) Interval Type-2 Takagi-Sugeno-Kang (TSK) Fuzzy Logic System (IT2FLS) for function approximation under noisy circumstances. Firstly, sufficient conditions on the antecedent and the consequent parameters of the IT2FLS are given to ensure that three kinds of prior knowledge monotonicity, symmetry and special points, can be embedded into the IT2FLS. And then, we use three optimization algorithms constrained least squares algorithm, active-set algorithm and hybrid learning algorithm to design the IT2FLS, respectively. The effectiveness of the three algorithms and the comparisons of their performance are demonstrated by simulation examples.
Keywords :
function approximation; fuzzy logic; fuzzy reasoning; fuzzy systems; learning (artificial intelligence); least squares approximations; optimisation; IT2FLS; TSK fuzzy system; Takagi-Sugeno-Kang; active set algorithm; antecedent parameters; circumstance parameters; constrained least squares algorithm; function approximation; hybrid learning algorithm; interval type-2 fuzzy logic systems; optimization; prior knowledge; single input single output; Algorithm design and analysis; Fuzzy logic; Learning systems; Linear matrix inequalities; Minimization; Optimization; Silicon; interval type-2 fuzzy logic system; optimization algorithm; prior knowledge;
Conference_Titel :
Fuzzy Systems (FUZZ), 2011 IEEE International Conference on
Conference_Location :
Taipei
Print_ISBN :
978-1-4244-7315-1
Electronic_ISBN :
1098-7584
DOI :
10.1109/FUZZY.2011.6007364