A geometric approach for solving fuzzy linear programming problems

In this paper we first recall some definitions and results of fuzzy plane geometry, and then introduce some definitions in the geometry of two-dimensional fuzzy linear programming (FLP). After defining the optimal solution based on these definitions, we use the geometric approach for obtaining optimal solution(s) and show that the algebraic solutions obtained by Zimmermann method (ZM) and our geometric solutions are the same. Finally, numerical examples are solved by these two methods.     

Fuzzy度量空间扩张型映象的不动点定理

本文给出Ｆｕｚｚｙ度量空间一些扩张型映象的不动点定理,这些结果发展和改进了普通度量空间中相应的结果。     

无解的模糊关系方程的最优近似解

对无解的模糊关系方程给出了最优近似解的定义,证明了最优近似解的存在性,给出了求最优近似解的算法     

Fuzzy codes and distance properties

A fuzzy code is defined as a fuzzy subset of n-tuples over a set F. An analysis of the Hamming distance between two fuzzy codewords and the error-correcting capability of a regular code in terms of its corresponding fuzzy code is presented.     

Fuzzy dynamical systems

A fuzzy dynamical system on an underlying complete, locally compact metric state space X is defined axiomatically in terms of a fuzzy attainability set mapping on X. This definition includes as special cases crisp single and multivalued dynamical systems on X. It is shown that the support of such a fuzzy dynamical system on X is a crisp multivalued dynamical system on X, and that such a fuzzy dynamical system can be considered as a crisp dynamical system on a state space of nonempty compact fuzzy subsets of X. In addition fuzzy trajectories are defined, their existence established and various properties investigated.     

A characterization of fuzzy subgroups

An ordinary subgroup of a group G is (1) a subset of G, (2) closed under the group operation. In a fuzzy subgroup it is precisely these two notions that lose their deterministic character. A fuzzy subgroup μ of a group (G,·) associates with each group element a number, the larger the number the more certainly that element belongs to the fuzzy subgroup. The closure property is captured by the inequality μ(x · y)?T(μ(x), μ(y)). In A. Rosenfeld's original definition, T was the function ‘minimum’. However, any t-norm T provides a meaningful generalization of the closure property. Two classes of fuzzy subgroups are investigated. The fuzzy subgroups in one class are subgroup generated, those in the other are function generated. Each fuzzy subgroup in these classes satisfies the above inequality with T given by T(a, b) = max(a + b ?1, 0). While the two classes look different, each fuzzy subgroup in either is isomorphic to one in the other. It is shown that a fuzzy subgroup satisfies the above inequality with $\text{T =}\text{‘minimum’}$ if and only if it is subgroup generated of a very special type. Finally, these notions are applied to some abstract pattern recognition problems.     

Performance investigation of an advanced tracked prime mover on the low bearing soil

This study has presented a performance investigation of an advanced tracked prime mover for the operation of agricultural goods and other operations on peat terrain. The maximum and continuous traction torque of the prime mover has been developed by designing an advanced controller for controlling the intelligent system. The prime mover’s mobility is studied with ultrasonic displacement sensor, torque transducer, National Instrument cRIO-9004 Compact-RIO Real-time Controller Unit (RCU), a National Instrument TPC 2106T Touch Panel Control (TPC), a Trimble AG132 GPS antenna and receiver unit, and a Dlink DIR-655 router. The fuzzy logic controller (FLC) has been equipped with the prime mover hydraulic system to increase the traction torque of the hydraulic motor when the prime mover’s sinkage is more than or equal to 70 mm. The prime mover’s design demonstrates good potential in traversing peat terrain as the measured tractive effort was found to be 48% of the vehicle’s gross weight while the recommended tractive effort is in the range of 30–36% of the vehicle’s gross weight.     

On a family of connectives for fuzzy sets

In this paper we find the general solution of the functional equation $\text{S}{}^1\text{(T(x, y), T(x, N(y))) = x}$, where $\text{S}{}^1$ is a t-conorm, T is a t-norm and N is a strong negation on the unit interval. In particular the result yields a family of connectives for fuzzy sets.     

Testing several nonnested regressions simultaneously. A nonparametric approach

A test for the problem of choosing between several nonnested nonlinear regression models simultaneously is presented. The test does not require an explicit specification of a parametric family of distributions for the error term and has a closed form.