Evolutionary Design of Fuzzy Inference Systems by Means of Fuzzy Partition of Input Space
نویسندگان
چکیده
In this paper, we introduce the evolutionary design methodology of fuzzy inference systems by means of fuzzy partition of input space. The rules of the proposed fuzzy model are realized with the aid of the fuzzy partition of input space generated by fuzzy c-means clustering algorithm. The number of the partition of input space is equal to the number of clusters. And the individual partitioned spaces describe the fuzzy rules. Due to these characteristics, we may alleviate the problem of the curse of dimensionality. The consequence part of the rule is represented by polynomial functions. We also consider the evolutionary optimization to determine the structure and estimate the values of the parameters of the model using realcoded genetic algorithms. Numerical examples are included to evaluate the performance of the proposed model.
منابع مشابه
A NOVEL FUZZY MULTI-OBJECTIVE ENHANCED TIME EVOLUTIONARY OPTIMIZATION FOR SPACE STRUCTURES
This research presents a novel design approach to achieve an optimal structure established upon multiple objective functions by simultaneous utilization of the Enhanced Time Evolutionary Optimization method and Fuzzy Logic (FLETEO). For this purpose, at first, modeling of the structure design problem in this space is performed using fuzzy logic concepts. Thus, a new problem creates with functio...
متن کاملSuccessive Optimization of Interval Type-2 Fuzzy C-Means Clustering Algorithm-based Fuzzy Inference Systems
A design methodology of interval type-2 fuzzy c-means clustering algorithm-based fuzzy inference systems (IT2FCMFIS) is introduced in this paper. An interval type-2 fuzzy c-means (IT2FCM) clustering algorithm is developed to generate the fuzzy rules in the form of the scatter partition of input space. And the individual partitioned spaces describe the fuzzy rules equal to the number of clusters...
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 کاملRobust Potato Color Image Segmentation using Adaptive Fuzzy Inference System
Potato image segmentation is an important part of image-based potato defect detection. This paper presents a robust potato color image segmentation through a combination of a fuzzy rule based system, an image thresholding based on Genetic Algorithm (GA) optimization and morphological operators. The proposed potato color image segmentation is robust against variation of background, distance and ...
متن کاملOptimization and design of Adaptive Neuro-Fuzzy Inference System using Particle Swarm Optimization and Fuzzy C-Means Clustering to predict the scour after bucket spillway
Additionally, if the materials at downstream of bucket spillway are erodible, the ogee spillway is likely to overturn by the time. Therefore, the prediction of the scour after bucket spillway is pretty important. In this study, the scour depths at downstream of bucket spillway are modeled using a new meta-heuristic model. This model is developed by combination of the Adaptive Neuro-Fuzzy Infere...
متن کامل