In this paper, a new approach has been proposed to identify and model the dynamics of a highly maneuverable aircraft through fuzzy logic algorithm. In general, aircraft flight dynamics is considered as a nonlinear and coupled system whose modeling through fuzzy logic algorithm can be done using a few experimental flight tests data. In this study, for identification and modeling of the aircraft ...

In this paper, we propose the least-squares method for computing the positive solution of a m × n fully fuzzy linear system (FFLS) of equations, where m > n, based on Kaffman’s arithmetic operations on fuzzy numbers that introduced in [18]. First, we consider all elements of coefficient matrix are non-negative or non-positive. Also, we obtain 1-cut of the fuzzy number vector solution of the non...

Growing fuzzy inference neural system (GFINN) is a fuzzy–neural network model. Its functionality can be expressed in a form of fuzzy if–then rules. The skill of the GFINN model to grow allows it to change its size and structure according to the training data. The resulting structure allows for a simple input features selection — not all input features have to be used in every fuzzy rule. The ne...

Nonparametric estimation capabilities of fuzzy systems in stochastic environments are analyzed in this paper. By using ideas from sieve estimation, increasing sequences of fuzzy rule-based systems, capable of consistently estimating regression surfaces in different settings, are constructed. Results include least squares learning of a mapping perturbed by additive random noise in a static-regre...

Modeling of yarn and fiber properties has been a popular topic in the field of textile engineering in recent decades. The common method for fitting models has been to use classical regression analysis, based on the assumptions of data crispness and deterministic relations among variables. However, in modeling practical systems such as cotton spinning, the above assumptions may not hold true. Pr...

In least squares support vector machines (LS-SVMs), the optimal separating hyperplane is obtained by solving a set of linear equations instead of solving a quadratic programming problem. But since SVMs and LS-SVMs are formulated for two-class problems, unclassifiable regions exist when they are extended to multiclass problems. In this paper, we discuss fuzzy LS-SVMs that resolve unclassifiable ...