The parameter identification of magnetorheological dampers by an inverse method is proposed. A modified Bouc-Wen modified dynamic model is considered and its parameters are obtained by using genetic algorithms. The experimental data consist of time histories of current, displacement, velocity, and force measured for both constant and variable current. The model parameters are determined using a set of experimental measurements corresponding to different constant current values and the resulting model is validated on the data measured for variable current. Based on this model a semi-active control of vehicle suspension is studied and a fuzzy controller is developed to reduce the chattering effect.

In this paper is proposed an extended Bouc-Wen model for improving its capability to approximate experimental symmetric hysteretic loops. On the basis of the generalized equation there are defined integral and differential conditions that describe the essential geometric properties of a hysteretic curve. Next, a new method based on Genetic Algorithms is developed to identify the Bouc-Wen model parameters from experimental hysteretic loops obtained from periodic loading tests. The performance of presented approach is illustrated for two types of seismic protection devices with hysteretic characteristics: elastomeric base isolators and buckling restrained dissipative braces. The applicability of proposed method is highlighted by using the derived models to analyse by numerical simulation the efficiency of these devices for reducing seismic response of a three stories civil structure.

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Previous research proposed the uniform mutation inside the sphere as a new mutation operator for evolution strategies (continuous evolutionary algorithms), with case study the elitist algorithm on the SPHERE. For that landscape, one-step success probability and expected progress were estimated analytically, and further proved to converge, as space dimension increases, to the corresponding asymptotics of the algorithm with normal mutation. This paper takes the analysis further by considering the RIDGE, an asymmetric landscape almost uncovered in literature. For the elitist algorithm, estimates of expected progress along the radial and longitudinal axes are derived, then tested numerically against the real behavior of the algorithm on several functions from this class. The global behavior of the algorithm is predicted correctly by iterating the one-step analytical formulas. Moreover, experiments show identical mean value dynamics for the algorithms with uniform and normal mutation, which implies that the derived formulas apply also to the normal case. Essential to the whole analysis is θ, the inclination angle of the RIDGE. The behavior of the algorithm on the SPHERE and HYPERPLANE is also obtained, at the limits of the θ interval (0 • , 90 • ].

Cellular automata are binary lattices used for modeling complex dynamical systems. The automaton evolves iteratively from one configuration to another, using some local transition rule based on the number of ones in the neighborhood of each cell. With respect to the number of cells allowed to change per iteration, we speak of either synchronous or asynchronous automata. If randomness is involved to some degree in the transition rule, we speak of probabilistic automata, otherwise they are called deterministic. With either type of cellular automaton we are dealing with, the main theoretical challenge stays the same: starting from an arbitrary initial configuration, predict (with highest accuracy) the end configuration. If the automaton is deterministic, the outcome simplifies to one of two configurations, all zeros or all ones. If the automaton is probabilistic, the whole process is modeled by a finite homogeneous Markov chain, and the outcome is the corresponding stationary distribution. Based on our previous results for the asynchronous case-connecting the probability of a configuration in the stationary distribution to its number of zero-one borders-the article offers both numerical and theoretical insight into the long-term behavior of synchronous cellular automata.