مقاله انگلیسی رایگان در مورد طراحی سیستم های منطق فازی نوع 2 بر اساس بهینه سازی کلونی مورچه – اسپرینگر 2019

1- INTRODUCTION

2- THE BASIC CONCEPTS OF IMPROVED ANT COLONY OPTIMIZATION

3- THE BASIC CONCEPTS AND FRAMEWORK OF INTERVAL TYPE-2 TSK TYPE FUZZY LOGIC SYSTEM

4- THE DESIGN OF A2-C1 TYPE INTERVAL TSK FUZZY LOGIC SYSTEM BASED ON IACO

5- APPLICATION EXAMPLES

6- CONCLUSION

REFERENCES

Abstract

An Improved Ant Colony Optimization (IACO) is proposed to design A2-C1 type fuzzy logic system (FLS) in the paper. The design includes parameters adjustment and rules selection, and the performance of the intelligent fuzzy system, which can be improved by choosing the most optimal parameters and reducing the redundant rules. In order to verify the feasibility of the proposed algorithm, the intelligence fuzzy logic systems based on the algorithms are applied to predict the Mackey-Glass chaos time series. The simulations show that both the IACO and ACO have better tracking performances. The results compared with classical algorithm BP ( back-propagation design) shows the tracking performance of IACO is more precise, the result compared with ACO shows that either the training result or the testing result, the tracking performance of IACO is better, and IACO has a faster convergence rate than ACO, the results compared with the Intelligent type-1 fuzzy logic systems show that both the A2-C1 type FLS based on IACO and ACO have better tracking performance than type-1 fuzzy logic system.

INTRODUCTION

Nowadays, fuzzy logic system is applied into a variety of fields. Professor L. A. Zadeh proposed the conception of fuzzy set first time [1], from that time on, fuzzy logic system attracts a large number of scholars to research. Although, fuzzy logic systems have some advantages, but there are several shortcomings, the main shortcomings are that it is too difficult to obtain the optimal parameters and rule explosion. Recent years, it has been a research hot spot that adopting intelligent algorithms to design of fuzzy logic system, respectively. Lian et al. optimized the radial basis function neural network based on QPSO, and the result was better than BP algorithm and least square method [2]. Zhai et al. combined the QPSO with the non single point interval type-2 fuzzy logic system and used to design of image noise filter, the result was better than traditional non fuzzy methods [3]. Yazdi combined GA with fuzzy logic system, and it was applied to the optimization of off center braced frame system, compared with the structure optimized only by GA, the effect was more remarkable [4]. Juang et al. combined ant colony optimization with fuzzy cluster to design fuzzy logic system, and used for nonlinear plant tracking control, compared with PSO and GA, the results were more better [5].