Type-2 fuzzy neural networks with fuzzy clustering and differential evolution optimization

Type-2 Fuzzy Multiplication Wavelet Neural Network Model Design for Classification of Breast Cancer

“…Conventionally, design factors are determined for once for the whole rule base in the previous papers [16,18,25,36]…”

“…However, type-2 fuzzy logic systems give better results in many areas. They have been applied to many different applications such as identification of nonlinear systems [16][17][18][19][20][21], control [22,23], time series prediction [24], system modeling [20,25,26], stock price prediction [27] and control of mobile robots [28,29]. In [30], a review of type-2 fuzzy logic applications is presented for pattern recognition.…”

Type-2 Fuzzy G-Tolerance Relation and Its Properties

“…The advantage of type-2 fuzzy sets is that they are helpful in some cases where it is difficult to find the exact membership functions for a fuzzy sets. There are wide variety of applications of type-2 fuzzy sets in science and technology like computing with words [14], human resource management [9], forecasting of time-series [11], clustering [1,17], pattern recognition [3], fuzzy logic controller [20], industrial application [4], simulation [18], neural network [2], [19], and transportation problem [13].…”

Genetic generation of fuzzy knowledge bases: new perspectives

“…In this sense, many approaches have been applied for the automatic generation of fuzzy systems from data. Neural networks (Aliev et al, 2011), genetic algorithms (Herrera, 2008), and, more recently, other bio-inspired approaches, such as particle swarm optimization (Al-Aawar et al, 2011;Pousinho et al, 2011), ant colony optimization (Ahmadizar and Soltanpanah, 2011), and artificial immunological systems (Prakash and Deshmukh, 2011) are among the most successful techniques for the automatic generation of fuzzy systems.…”

“…The use of artificial neural networks is explored in (Aliev et al, 2011) for the generation of fuzzy systems. The proposed method includes the optimization of the fuzzy data base by means of neural networks.…”