This article describes a multiobjective spatial fuzzy clustering algorithm for image segmentation. To obtain satisfactory segmentation performance for noisy images, the proposed method introduces the non-local spatial information derived from the image into fitness functions which respectively consider the global fuzzy compactness and fuzzy separation among the clusters. After producing the set...

In this paper, the notion of almost S^{*}-compactness in L-topologicalspaces is introduced following Shi’s definition of S^{*}-compactness. The propertiesof this notion are studied and the relationship between it and otherdefinitions of almost compactness are discussed. Several characterizations ofalmost S^{*}-compactness are also presented.

in current study, seven organic fertilizers including; leaf mold (lm), rice husk (rh), manure, spent mushroom compost (smc), mixture of lm, rh and smc (m 1), mixture of lm, rh and manure (m 2) with the ratio of 1:1:1 and control (no fertilizer) along with some levels of soil compactness comprising roller weights of 36, 56, 76 kilogram were used. treatments were applied in a strip plot design wi...

In our previous work we showed that genetic algorithms (GAs) provide a powerful tool to increase the accuracy of fuzzy models for both systems modeling and classification. In addition to these results, we explore the GA to find redundancy in the fuzzy model for the purpose of model reduction. An aggregated similarity measure is applied to search for redundancy in the rule base description. As a...

A novel hierarchical coevolutionary approach called HiCEFS for the dynamic generation of a fuzzy system from data is presented. This paper is focused on using the proposed hierarchical coevolutionary approach to generate a form of generic membership function (MF) called Irregular Shaped Membership Function (ISMF). This approach divides the ISMFs generation task into several subtasks of finding ...

|Two training algorithms for self evolving neural networks are discussed for rule based data analysis. E cient classi cation is achieved with less number of automatically added clusters and application data is analysed by interpreting the trained neural network as a fuzzy rule based system. Learning Vector Quantisation algorithm has been modied acquiring the self evolvement character in the pro...