Securing Interpretability of Fuzzy Models for Modeling Nonlinear Mimo Systems Using a Hybrid of Evolutionary Algorithms
نویسندگان
چکیده
In this study, a Multi-Objective Genetic Algorithm (MOGA) is utilized to extract interpretable and compact fuzzy rule bases for modeling nonlinear Multi-input Multi-output (MIMO) systems. In the process of nonlinear system identification, structure selection, parameter estimation, model performance and model validation are important objectives. Furthermore, securing low-level and high-level interpretability requirements of fuzzy models is especially a complicated task in case of modeling nonlinear MIMO systems. Due to these multiple and conflicting objectives, MOGA is applied to yield a set of candidates as compact, transparent and valid fuzzy models. Also, MOGA is combined with a powerful search algorithm namely Differential Evolution (DE). In the proposed algorithm, MOGA performs the task of membership function tuning as well as rule base identification simultaneously while DE is utilized only for linear parameter identification. Practical applicability of the proposed algorithm is examined by two nonlinear system modeling problems used in the literature. The results obtained show the effectiveness of the proposed method.
منابع مشابه
SECURING INTERPRETABILITY OF FUZZY MODELS FOR MODELING NONLINEAR MIMO SYSTEMS USING A HYBRID OF EVOLUTIONARY ALGORITHMS
In this study, a Multi-Objective Genetic Algorithm (MOGA) is utilized to extract interpretable and compact fuzzy rule bases for modeling nonlinear Multi-input Multi-output (MIMO) systems. In the process of non- linear system identi cation, structure selection, parameter estimation, model performance and model validation are important objectives. Furthermore, se- curing low-level and high-level ...
متن کاملSoft Computing Methods based on Fuzzy, Evolutionary and Swarm Intelligence for Analysis of Digital Mammography Images for Diagnosis of Breast Tumors
Soft computing models based on intelligent fuzzy systems have the capability of managing uncertainty in the image based practices of disease. Analysis of the breast tumors and their classification is critical for early diagnosis of breast cancer as a common cancer with a high mortality rate between women all around the world. Soft computing models based on fuzzy and evolutionary algorithms play...
متن کاملNovel Hybrid Fuzzy-Evolutionary Algorithms for Optimization of a Fuzzy Expert System Applied to Dust Phenomenon Forecasting Problem
Nowadays, dust phenomenon is one of the important challenges in warm and dry areas. Forecasting the phenomenon before its occurrence helps to take precautionary steps to prevent its consequences. Fuzzy expert systems capabilities have been taken into account to assist and cope with the uncertainty associated to complex environments such as dust forecasting problem. This paper presents novel hyb...
متن کاملNovel Hybrid Fuzzy-Evolutionary Algorithms for Optimization of a Fuzzy Expert System Applied to Dust Phenomenon Forecasting Problem
Nowadays, dust phenomenon is one of the important challenges in warm and dry areas. Forecasting the phenomenon before its occurrence helps to take precautionary steps to prevent its consequences. Fuzzy expert systems capabilities have been taken into account to assist and cope with the uncertainty associated to complex environments such as dust forecasting problem. This paper presents novel hyb...
متن کاملProposing a Novel Cost Sensitive Imbalanced Classification Method based on Hybrid of New Fuzzy Cost Assigning Approaches, Fuzzy Clustering and Evolutionary Algorithms
In this paper, a new hybrid methodology is introduced to design a cost-sensitive fuzzy rule-based classification system. A novel cost metric is proposed based on the combination of three different concepts: Entropy, Gini index and DKM criterion. In order to calculate the effective cost of patterns, a hybrid of fuzzy c-means clustering and particle swarm optimization algorithm is utilized. This ...
متن کامل