Analysis of Data Generated From Multidimensional Type-1 and Type-2 Fuzzy Membership Functions

2018 ◽  
Vol 26 (2) ◽  
pp. 681-693 ◽  
Author(s):  
Desh Raj ◽  
Aditya Gupta ◽  
Bhuvnesh Garg ◽  
Kenil Tanna ◽  
Frank Chung-Hoon Rhee
Keyword(s):  
Fuzzy Membership ◽  
Membership Functions ◽  
Fuzzy Membership Functions

scholarly journals Automatic Methods for Generation of Type-1 and Interval Type-2 Fuzzy Membership Functions

2015 ◽  
Vol 11 (9) ◽  
pp. 976-987 ◽  
Author(s):  
Andréia Alves dos Santos Schwaab ◽  
Silvia Modesto Nassar ◽  
Paulo José de Freitas Filho
Keyword(s):  
Fuzzy Membership ◽  
Membership Functions ◽  
Fuzzy Membership Functions ◽  
Automatic Methods ◽  
Interval Type

An approach for parameterized shadowed type-2 fuzzy membership functions applied in control applications

2018 ◽  
Vol 23 (11) ◽  
pp. 3887-3901 ◽  
Author(s):  
Patricia Melin ◽  
Emanuel Ontiveros-Robles ◽  
Claudia I. Gonzalez ◽  
Juan R. Castro ◽  
Oscar Castillo
Keyword(s):  
Fuzzy Membership ◽  
Membership Functions ◽  
Control Applications ◽  
Fuzzy Membership Functions

scholarly journals INTERVAL TYPE-2 FUZZY BASED NEURAL NETWORK FOR HIGH RESOLUTION REMOTE SENSING IMAGE SEGMENTATION

2016 ◽  
Vol XLI-B7 ◽  
pp. 385-391 ◽  
Author(s):  
Chunyan Wang ◽  
Aigong Xu ◽  
Chao Li ◽  
Xuemei Zhao
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
High Resolution ◽  
Fuzzy Model ◽  
Remote Sensing Image ◽  
Fuzzy Membership ◽  
Fuzzy Membership Functions ◽  
Interval Type

Recently, high resolution remote sensing image segmentation is a hot issue in image procesing procedures. However, it is a difficult task. The difficulties derive from the uncertainties of pixel segmentation and decision-making model. To this end, we take spatial relationship into consideration when constructing the interval type-2 fuzzy neural networks for high resolution remote sensing image segmentation. First, the proposed algorithm constructs a Gaussian model as a type-1 fuzzy model to describe the uncertainty contained in the image. Second, interval type-2 fuzzy model is obtained by blurring the mean and variance in type-1 model. The proposed interval type-2 model can strengthen the expression of uncertainty and simultaneously decrease the uncertainty in the decision model. Then the fuzzy membership function itself and its upper and lower fuzzy membership functions of the training samples are used as the input of neuron network which acts as the decision model in proposed algorithm. Finally, the relationship of neighbour pixels is taken into consideration and the fuzzy membership functions of the detected pixel and its neighbourhood are used to decide the class of each pixel to get the final segmentation result. The proposed algorithm, FCM and HMRF-FCM algorithm and an interval type-2 fuzzy neuron networks without spatial relationships are performed on synthetic and real high resolution remote sensing images. The qualitative and quantitative analyses demonstrate the efficient of the proposed algorithm, especially for homogeneous regions which contains a great difference in its gray level (for example forest).

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