As a generalization of the triple I method, the universal triple I method is investigated from the viewpoints of both fuzzy reasoning and fuzzy controller. The universal triple I principle is put forward, which improves the previous triple I principle. Then, unified form of universal triple I method is established based on the (0, 1) -implication or R-implication. Moreover, the reversibility property of universal triple I method is analyzed from expansion, reduction and other type operators, which demonstrate that its reversibility property seems ne, especially for the case employing the (0, 1) -implication. Lastly, we analyze the response ability of fuzzy controllers based on universal triple I method, then the practicability of triple I method is improved.

In the beginning, we describe the fuzzy inner product space and the fuzzy Hilbert space. Our goal is to use the fuzzy reproducing kernel method to solve the second-order fuzzy boundary value problem. The fuzzy convergence analysis of introduced method is discussed in detail. We present some examples in the end.

There are some situations in the real word which can not be modeled by the proposed method until now, to get rid of this difficulty, the concept of fuzzy Differential Inclusion (FDI) is introduced and then an extension of differential transformation method for solving it is given by defining a fuzzy partition. Proposed algorithms are illustrated by numerical example.

In this paper, the fuzzy bivariate Chebyshev method is proposed for solving the fuzzy Volterra-Fredholm integral equations (FVFIE). FVFTE is converted to a dual fuzzy linear system that can be solved by the proposed method in [10]. And finally, the method is explained with illustrative examples.

In this paper, a numerical method for solving 'fuzzy differential inclusions' is considered.The fuzzy reachable set can be approximated by the proposed method with complete error analysis which is discussed in detail. Moreover, the extrapolation method is employed for increasing the accuracy of the approximate solution. The method is illustrated by solving some linear and nonlinear fuzzy initial value problems.

The manufacturing sector is one of the principal components in economic development plans. Identifying the key sectors and “ take-off” industries as the driving force behind economic growth is crucial for investment priorities and resource allocation. Accordingly, the main objective of this research is to rank and identify key sectors based on ISIC Dual Codes in 23 sub-sectors of Iranian manufacturing industries. The data given by the Statistical Centre of Iran was implemented by surveying manufacturing establishments with 10 or more workers during the period from 2008 to 2013. Selected industries were ranked by forming a composite index based on 9 criteria, which were obtained by using the FBWM-VIKOR methods. These criteria consist of: export value, labor service compensation, energy efficiency, labor productivity, investment, added value, production value, number of workshops, and the number of employees. The results indicate that, chemical production, food and drinking, and basic metals industries could be the leading sectors in promoting Iranian economical growth.

fuzzy Newton-Cotes method for integration of fuzzy functions that was proposed by Ahmady in [1]. In this paper we construct error estimate of fuzzy Newton-Cotes method such as fuzzy Trapezoidal rule and fuzzy Simpson rule by using Taylor's series. The corresponding error terms are proven by two theorems. We prove that the fuzzy Trapezoidal rule is accurate for fuzzy polynomial of degree one and fuzzy Simpson rule is accurate for polynomial of degree three. The accuracy of fuzzy Trapezoidal rule and fuzzy Simpson rule for integration of fuzzy functions are illustrated by two examples.