Randomly generating test problems for fuzzy relational equations

Fuzzy relational equations play an important role in fuzzy set theory and fuzzy logic systems. To compare and evaluate the accuracy and efficiency of various solution methods proposed for solving systems of fuzzy relational equations as well as the associated optimization problems, a test problem random generator for systems of fuzzy relational equations is needed. In this paper, procedures for generating test problems of fuzzy relational equations with the sup-T{\mathcal{T}} composition are proposed for the cases of sup-T_M{\mathcal{T}_M}, sup-T_P{\mathcal{T}_P}, and sup-T_L{\mathcal{T}_L } compositions. It is shown that the test problems generated by the proposed procedures are consistent. Some properties are discussed to show that the proposed procedures randomly generate systems of fuzzy relational equations with various number of minimal solutions. Numerical examples are included to illustrate the proposed procedures.     

A survey on fuzzy relational equations, part I: classification and solvability

Fuzzy relational equations play an important role in fuzzy set theory and fuzzy logic systems, from both of the theoretical and practical viewpoints. The notion of fuzzy relational equations is associated with the concept of “composition of binary relations.” In this survey paper, fuzzy relational equations are studied in a general lattice-theoretic framework and classified into two basic categories according to the duality between the involved composite operations. Necessary and sufficient conditions for the solvability of fuzzy relational equations are discussed and solution sets are characterized by means of a root or crown system under some specific assumptions.     

On measures of fuzziness of solutions of fuzzy relation equations with generalized connectives

By using a general class of fuzzy connectives of Yager [Fuzzy Sets and Systems4 (1980), 235–242], Pedrycz [Fuzzy relational equations with generalized convectives and their applications, Fuzzy Sets and Systems10 (1983), 185–201] has shown that the classical fuzzy relation equations of Sanchez [in “Fuzzy Automata and Decision Processes” (M. M. Gupta, G. N. Saridis, and B. R. Gaines, Eds.), pp. 221–234, North-Holland, Amsterdam, 1977] can be considered as a particular case of a more extensive class of fuzzy equations. For such types of equations, in this paper the solutions having the greatest energy measure and the smallest possible entropy measure of fuzziness are characterized.     

Evaluation of fully fuzzy matrix equations by fuzzy neural network

In this paper, a new hybrid method based on fuzzy neural network for approximate solution of fully fuzzy matrix equations of the form AX=D$\mathit{AX}=D$, where A and D are two fuzzy number matrices and the unknown matrix X is a fuzzy number matrix, is presented. Then, we propose some definitions which are fuzzy zero number, fuzzy one number and fuzzy identity matrix. Based on these definitions, direct computation of fuzzy inverse matrix is done using fuzzy matrix equations and fuzzy neural network. It is noted that the uniqueness of the calculated fuzzy inverse matrix is not guaranteed. Here a neural network is considered as a part of a large field called neural computing or soft computing. Moreover, in order to find the approximate solution of fuzzy matrix equations that supposedly has a unique fuzzy solution, a simple algorithm from the cost function of the fuzzy neural network is proposed. To illustrate the easy application of the proposed method, numerical examples are given and the obtained results are discussed.     

On the unique solvability of fuzzy relational equations

The solution set of a consistent system of fuzzy relational equations with max-min composition can be characterized by one maximum solution and a finite number of minimal solutions. A polynomial-time method of O(mn) complexity is proposed to determine whether such a system has a unique minimal solution and/or a unique solution, where m, n are the dimensions of the input data. The proposed method can be extended to examining a system of fuzzy relational equations with max-T composition where T is a continuous triangular norm.     

Solvability of fuzzy relational equations and manipulation of fuzzy data

The problem of characterization of fuzzy relational equations with respect to their solvability property is studied. Some aspects of manipulation of fuzzy data with the aid of fuzzy relational equations are considered. Two stages of manipulation process are indicated: combining pieces of evidence and inferring their mutual correspondence. Both of them are formulated and solved by the use of fuzzy relational equations. The role of a solvability index is extensively studied. Numerical considerations give an illustration of the approach we propose and deal with some real data used for fuzzy controllers.     

On the fuzzy initial value problem

The initial value problem x′(t) = f(t,x(t)), x(0)= x₀, with fuzzy initial value and with deterministic or fuzzy function f is considered. Two different approaches, viz. the extension principle and the use of extremal solutions of deterministic initial value problems, are applied. Generalizations to fuzzy integral equations and fuzzy functional differential equations are indicated.     

Who's who in fuzzy sets

In this paper some fuzzy relation equations provided with one solution on a finite set are characterized: we consider fuzzy relation equations H ° Q = T with Q?F(XxY) and card X ? cardY. After recalling the definition of equivalent fuzzy relation equations, we introduce the definition of ρ-equivalent ones, which allows us to constrain our research without loss of generality to fuzzy relation equations where T does not have zero-components.     

Control problems in fuzzy systems

This paper deals with some problems of the control of fuzzy systems described by means of a relational equation of the type: X_{κ + 1} = U_κ ° X_κ ° R. The basic aspects such as mutual identification and control, stabilization control, control with constraints, generation of control rules of a fuzzy logic controller, important from the theoretical and applicational point of view are discussed and illustrated by means of numerical examples.     

Existence and uniqueness of fuzzy solution for the nonlinear fuzzy integrodifferential equations

In this paper, existence and uniqueness of fuzzy solution for the nonlinear fuzzy integrodifferential equations is established via Banach fixed-point analysis approach and using the fuzzy number whose values are normal, convex, upper semicontinuous and compactly supported interval in E_N.     

Solution to fuzzy system of linear equations with crisp coefficients

This paper presents a new and simple method to solve fuzzy real system of linear equations by solving two n × n crisp systems of linear equations. In an original system, the coefficient matrix is considered as real crisp, whereas an unknown variable vector and right hand side vector are considered as fuzzy. The general system is initially solved by adding and subtracting the left and right bounds of the vectors respectively. Then obtained solutions are used to get a final solution of the original system. The proposed method is used to solve five example problems. The results obtained are also compared with the known solutions and found to be in good agreement with them.     

A note on solution sets of interval-valued fuzzy relational equations

This note discusses three types of solutions for a system of interval-valued fuzzy relational equations with max-T composition and illustrates their relations to the solutions of a system of fuzzy relational inequalities with max-T composition. It validates the major claims appeared in Fuzzy Optimization and Decision Making, 2 (2003) 41–60; 4 (2005) 331–349.     

On direct and inverse problems for fuzzy equation systems with tolerances

We investigate the resolution of fuzzy (relational) equation systems with tolerances which are a certain extension of fuzzy equations considered f.i. in [3–5]. The extension of the concept of Higashi and Klir [3] enables us to describe the set of solutions to our problem (for given tolerances) by means of posets. In a second part we investigate an inverse problem: Given upper (lower) tolerances how to determine lower (upper) tolerances such that the arising problem becomes consistent? Numerical examples are given.     

Fuzzy relational neural network

In this paper a fuzzy neural network based on a fuzzy relational “IF-THEN” reasoning scheme is designed. To define the structure of the model different t-norms and t-conorms are proposed. The fuzzification and the defuzzification phases are then added to the model so that we can consider the model like a controller. A learning algorithm to tune the parameters that is based on a back-propagation algorithm and a recursive pseudoinverse matrix technique is introduced. Different experiments on synthetic and benchmark data are made. Several results using the UCI repository of Machine learning database are showed for classification and approximation tasks. The model is also compared with some other methods known in literature.     

Numerical solution of algebraic fuzzy equations with crisp variable by Gauss–Newton method

In this paper we introduce an algebraic fuzzy equation of degree n with fuzzy coefficients and crisp variable, and we present an iterative method to find the real roots of such equations, numerically. We present an algorithm to generate a sequence that can be converged to the root of an algebraic fuzzy equation.     

Some remarks on the identification problem in fuzzy systems

Several identification problems in fuzzy systems are considered which are described by means of fuzzy relational equations. The determination of a family of fuzzy relations of the system is described in detail.     

Simulation and evaluation of dual fully fuzzy linear systems by fuzzy neural network

In this paper, a novel hybrid method based on fuzzy neural network for approximate solution of fuzzy linear systems of the form Ax = Bx + d, where A and B are two square matrices of fuzzy coefficients, x and d are two fuzzy number vectors, is presented. Here a neural network is considered as a part of a large field called neural computing or soft computing. Moreover, in order to find the approximate solution, a simple and fast algorithm from the cost function of the fuzzy neural network is proposed. Finally, we illustrate our approach by some numerical examples.     

Remarks on the Interest-relative Theory of Vagueness

I discuss the interest-relative account of vagueness and argue for a distinction between relational vague predicates and non-relational vague predicates depending on the kind of properties expressed by them. The strategy rests on three arguments arising from the existence of clear cases of a vague predicate, from contexts in which a different answer is required for questions about whether a vague predicate applies to an item, and whether such an item satisfies the interest of an agent, and from cases where an object changes up to the point of becoming P, where P is a vague predicate. In the second part of the paper, I distinguish between relational properties and non-relational properties, and I argue for the view that some vague predicates can express non-relational properties, comparative relational properties and interest-relative properties. On the basis of these arguments, I conclude that vagueness cannot be reduced to interest-relativity.     

Modeling and control of nonlinear systems using novel fuzzy wavelet networks: The output adaptive control approach

This paper proposes an observer based self-structuring robust adaptive fuzzy wave-net (FWN) controller for a class of nonlinear uncertain multi-input multi-output systems. The control signal is comprised of two parts. The first part arises from an adaptive fuzzy wave-net based controller that approximates the system structural uncertainties. The second part comes from a robust H_∞ based controller that is used to attenuate the effect of function approximation error and disturbance. Moreover, a new self structuring algorithm is proposed to determine the location of basis functions. Simulation results are provided for a two DOF robot to show the effectiveness of the proposed method.     

Type-2 fuzzy fractional derivatives

In this paper, we introduce two definitions of the differentiability of type-2 fuzzy number-valued functions of fractional order. The definitions are in the sense of Riemann–Liouville and Caputo derivative of order β ∊ (0, 1), and based on type-2 Hukuhara difference and H₂-differentiability. The existence and uniqueness of the solutions of type-2 fuzzy fractional differential equations (T2FFDEs) under Caputo type-2 fuzzy fractional derivative and the definition of Laplace transform of type-2 fuzzy number-valued functions are also given. Moreover, the approximate solution to T2FFDE by a Predictor-Evaluate–Corrector-Evaluate (PECE) method is presented. Finally, the approximate solutions of two examples of linear and nonlinear T2FFDEs are obtained using the PECE method, and some cases of T2FFDEs applications in some sciences are presented.