Hedge algebras: an algebraic approach to structure of sets of linguistic truth values
Fuzzy Sets and Systems
Extended hedge algebras and their application to fuzzy logic
Fuzzy Sets and Systems
Aggregation operators and fuzzy systems modeling
Fuzzy Sets and Systems
Reasoning conditions on Ko´czy's interpolative reasoning method in sparse fuzzy rule bases
Fuzzy Sets and Systems
About simple fuzzy control and fuzzy control based on fuzzy relational equations
Fuzzy Sets and Systems
Practical Handbook of Genetic Algorithms
Practical Handbook of Genetic Algorithms
Application of Fuzzy Logic to Approximate Reasoning Using Linguistic Synthesis
IEEE Transactions on Computers
Genetic fuzzy logic controller: an iterative evolution algorithm with new encoding method
Fuzzy Sets and Systems
ICCOMP'06 Proceedings of the 10th WSEAS international conference on Computers
Fuzzy Linguistic Logic Programming
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Fuzzy linguistic logic programming and its applications
Theory and Practice of Logic Programming
Hi-index | 0.20 |
In previous papers, we introduced a new reasoning method based on quantifying linguistic domains, established a new fuzzy control algorithm, called hedge-algebras-based controller (HAC), and applied it to solve some fuzzy control problems. The HAC does not require fuzzy sets to provide the semantics of the linguistic terms used in the fuzzy rule system rather the semantics is obtained through the semantically quantifying mappings (SQMs). It was shown that the new method is very effective, i.e. for these problems it is always able to efficiently control the process toward the stable state. In the algebraic approach, the design of an HAC leads to the determination of the parameters of SQMs, which are the fuzziness measure of primary terms and linguistic hedges occurring in the fuzzy model, and the weights of a weighted averaging operator. However, there exists another problem, namely, how one can determine the optimal parameters of the method. In the present paper, we improved the HAC's design by adding an optimal step that aims to find the optimal parameters of HAC using a genetic algorithm (GA). To show the effectiveness of the proposed method, we apply it to solve again the inverted pendulum problem and the problem of holding an object on a ''hill''. The results demonstrate the good performance of the designed HAC.