DYNAMIC CHARACTERISTIC ANALYSIS OF FUZZY-STOCHASTIC TRUSS STRUCTURES BASED ON FUZZY FACTOR METHOD AND RANDOM FACTOR METHOD

A new fuzzy stochastic finite element method based on the fuzzy factor method and random factor method is given and the analysis of structural dynamic characteristic for fuzzy stochastic truss structures is presented. Considering the fuzzy randomness of the structural physical parameters and geometric dimensions simultaneously, the structural stiffness and mass matrices axe constructed based on the fuzzy factor method and random factor method; from the Rayleigh＇s quotient of structural vibration, the structural fuzzy random dynamic characteristic is obtained by means of the interval arithmetic; the fuzzy numeric characteristics of dynamic characteristic axe then derived by using the random variable＇s moment function method and algebra synthesis method. Two examples axe used to illustrate the validity and rationality of the method given. The advantage of this method is that the effect of the fuzzy randomness of one of the structural parameters on the fuzzy randomness of the dynamic characteristic can be reflected expediently and objectively.

A Dynamic Fuzzy Description Logic

Fuzzy description logics are considered as the logical infrastructure of fuzzy knowledge representation on the semantic Web. To deal with fuzzy and dynamic knowledge on the semantic Web and its applications, a new fuzzy extension of Attribute Language with Complement based on dynamic fuzzy logic called the dynamic fuzzy description logic （DFALC） is presented. The syntax and semantics of DFALC are formally defined, and the forms of axioms and assertions are specified. The DFALC provides more reasonable logic foundation for the semantic Web, and overcomes the insufficiency of using fuzzy description logic FALC to act as logical foundation for the semantic Web. The extended DFALC is more expressive than the existing fuzzy description logics and present more fuzzy information on the semantic Web.

A fuzzy crisis in a Duffing-van der Pol system

A crisis in a Duffing-van del Pol system with fuzzy uncertainties is studied by means of the fuzzy generalised cell mapping （FGCM） method. A crisis happens when two fuzzy attractors collide simultaneously with a fuzzy saddle on the basin boundary as the intensity of fuzzy noise reaches a critical point. The two fuzzy attractors merge discontinuously to form one large fuzzy attractor after a crisis. A fuzzy attractor is characterized by its global topology and membership function. A fuzzy saddle with a complicated pattern of several disjoint segments is observed in phase space. It leads to a discontinuous merging crisis of fuzzy attractors. We illustrate this crisis event by considering a fixed point under additive and multiplicative fuzzy noise. Such a crisis is fuzzy noise-induced effects which cannot be seen in deterministic systems.

State Observer Based Dynamic Fuzzy Logic System for a Class of SISO Nonlinear Systems

This paper presents an observer based dynamic fuzzy logic system （DFLS） scheme for a class of unknown single-input single-output （SISO） nonlinear dynamic systems with external disturbances. The proposed approach does not need the availability of the state variables. Within this scheme, the DFLS is employed to identify the unknown nonlinear dynamic system. The control law and parameter adaptation laws of the DFLS are derived based on Lyapunov synthesis approach. The control law is robustfied in H∞ sense to attenuate external disturbance, model uncertainties, and fuzzy approximation errors. It is shown that under appropriate assumptions, it guarantees the boundedness of all the signals in the closed-loop system and the asymptotic convergence to zero of tracking errors. The proposed method is applied to an inverted pendulum system to verify the effectiveness of the proposed algorithms.

A new approach to fuzzy dynamic fault tree analysis using the weakest n-dimensional t-norm arithmetic

Dynamic fault tree analysis is widely used for the reliability analysis of the complex system with dynamic failure characteristics. In many circumstances, the exact value of system reliability is difficult to obtain due to absent or insufficient data for failure probabilities or failure rates of components. The traditional fuzzy operation arithmetic based on extension principle or interval theory may lead to fuzzy accumulations. Moreover, the existing fuzzy dynamic fault tree analysis methods are restricted to the case that all system components follow exponential time-to-failure distributions. To overcome these problems, a new fuzzy dynamic fault tree analysis approach based on the weakest n-dimensional t-norm arithmetic and developed sequential binary decision diagrams method is proposed to evaluate system fuzzy reliability. Compared with the existing approach,the proposed method can effectively reduce fuzzy cumulative and be applicable to any time-tofailure distribution type for system components. Finally, a case study is presented to illustrate the application and advantages of the proposed approach.

Dynamic multicast sharing protection algorithm based on fuzzy game in multi-domain optical network

Aiming at the problem of dynamic multicast service protection in multi-domain optical network, this paper proposes a dynamic multicast sharing protection algorithm based on fuzzy game in multi-domain optical network. The algorithm uses the minimum cost spanning tree strategy and fuzzy game theory. First, it virtualizes two planes to calculate the multicast tree and the multicast protection tree respectively. Then, it performs a fuzzy game to form a cooperative alliance to optimize the path composition of each multicast tree. Finally, it generates a pair of optimal multicast work tree and multicast protection tree for dynamic multicast services. The time complexity of the algorithm is O(k3 m2 n), where n represents the number of nodes in the networks, k represents the number of dynamic multicast requests, and m represents the number of destination nodes for each multicast request. The experimental results show that the proposed algorithm reduces significantly the blocking rate of dynamic multicast services, and improves the utilization of optical network resources within a certain number of dynamic multicast request ranges.

A fuzzy model of predicting RNA secondary structure

We propose a novel model to predict RNA secondary structure based on the fuzzy sets theory. Through the fuzzy partition of state spaces and the incorporation of fuzzy goals, we can find the optimal fuzzy policy of the model using fuzzy dynamic programming algorithm effectively, and then determine optimal and suboptimal RNA secondary structures. Compared to the existing sophisticated prediction models, such as Zuker＇s method and the SCFG model, our fuzzy model based approach has many advantages： 1） computational complexity can be reduced by the fuzzy partition; 2） the optimal secondary structure and several suboptimal ones can be generated simultaneously; and 3） subjective prior knowledge can readily be incorporated. This paper presents a complete description of our fuzzy model and gives the implementation of the proposed method. We also apply the BJK fuzzy model structure to secondary structure predictions based on datasets of tRNA and tmRNA sequences. By the comparison of our fuzzy method with both the minimum free energy based mfold tool and the BJK grammar model of SCFG, our experimental results validate the effectiveness of the proposed method and the prediction accuracy is shown to be further improved.