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Copyright of Electric Machines & Control / Dianji Yu Kongzhi Xuebao is the property of Electric Machines & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Fuzzy linear programming is used widedly for the problems of uncertain system's optimization, but the optimization solutions or satisfactory solution are often not "immune" to parameters, that is if the parameters are changed, initial optimal solution is no longer optimal or even infeasible. First of all, for the λ-cut level of fuzzy linear programming, λ-robust optimization solutions are put forward; then article indicates the definition of λ-robust solution by fuzzy structured element, and obtains the solving model. Solution can achieve the degree requirements are different due to the different decision makers, therefore, the measure constraints which can reflect the decision-makers risk preferences are added to the model, the solution of this model is γ-robust solution, and the solution not only has the robustness and optimization, but also reflects the degree of risk preference of the decision makers. By an example, it is found that the optimization robust solutions with measurement are immune to parameters, which are more useful for decision-makers and reflect a better practical value. [ABSTRACT FROM AUTHOR]

This paper firstly introduced FastICA and RobustICA algorithms, and then researched the objective functions and optimization algorithms of these two algorithms. Furthermore, it analyzed and compared the performance of these two algorithms about robustness and computation complexity. As a result, the general performance of RobustICA is better than FastICA. [ABSTRACT FROM AUTHOR]

Aiming at the problem that bad topological layout leads to poor structure robustness and based on the H2 norm of system transfer function, quantitative evaluation index of structural robustness and robust configuration solving method were established firstly. Secondly, the relative densities of elements were chosen as design variables and the solid isotropic material with penalization (SIMP) model was used to describe the stiffness of the material, the robustness based structural design was transformed into continuum topology optimization. Then, the big bang-big crunch (BB-BC) method was improved by two-stage search and was used to solve the continuum topology optimization model. After that, taking cable supported hyperbolic flat latticed shell structure with four supports as an example, robust configurations under different load conditions were analyzed and the optimized effect of BB-BC method with different parameters was explored too. At last, comparative analysis between optimized model and conventionally designed model was conducted by static experiment and impact experiment. Results show that the two-stage BB-BC method can be well implemented for the robust design of cable supported structure, the improved BB-BC method can speed up the convergence, and arranging members based on the robust configuration can significantly increase the structure's mechanical property and anti-interference capability. [ABSTRACT FROM AUTHOR]

This paper presented a reliable guaranteed cost control for the control of the system output probability density functions (PDF) with which existed non-reliable controller using numerical solution optimization. It was noted that the performance index of the guaranteed cost was guaranteed to be more than a certain upper bound using the control algorithm. It gave a sufficient condition for the existence of reliable guaranteed cost algorithm using linear matrix inequality (LMI). Furthermore, it derived the optimal reliable guaranteed cost algorithm using a convex optimization algorithm. An illustrative example demonstrates that the system output probability density functions follows the given probability density functions and the well robustness and stability is obtain. [ABSTRACT FROM AUTHOR]

When an arbitrary unknown signal steering vector mismatch occurs or the training sample size is small, the performance of standard Capon beamforming (SCB) will be severely degraded. The existing algorithms are mostly made for the steering vector errors, with little considering the situations of small training sample size. In this paper, we develop a new approach, which is called vector optimization robust beamforming (VORB), to robust adaptive beamforming in the presence of an arbitrary unknown signal steering vector mismatch and small training sample size. Our approach is based on the vector optimization concept. It is shown that the proposed algorithm can be reformulated in a convex form as the so-called second-order cone (SOC) program and solved efficiently using the well-established optimization tool, Sedumi. It is also shown that the proposed technique can be interpreted in terms of diagonal loading, and how the optimization value of the diagonal loading is affected by different factors. Theory analysis and computer simulations show better performance of our robust beamformer as compared with existing adaptive beamforming algorithms: under different snapshots, SNRs and steering vector uncertainties, VORB can improve the output SINRs about 5 dB; under certain conditions, VORB has sharper spectrum peaks and lower sidelobe level (below - 15 dB). Finally, experimental results show that when VORB is applied to the spatial spectrum estimation in practical engineering, the robust high-resolution bearing estimation results can be obtained. [ABSTRACT FROM AUTHOR]

To solve the problem of poor flexibility and frequent route breakage caused by dynamic topology in Ad Hoc network routing protocols, a stable ant colony routing algorithm based on Q-learning (SACRQ) is proposed, which synthesizes the Ant Colony Optimization and the Q-learning algorithm. The pheromone level is equal to the Q value to enhance the learning ability of nodes. To avoid local peak, SARCQ applies an adaptive pseudo random proportional action choice rule to select the next hop. A new robustness of the links metric is presented to calculate the probability of the route selection together with the pheromone level. The algorithm enhances the stability of the links and demonstrates high flexibility to the dynamic topology of the network. Simulation results show that SACRQ achieves better performance in terms of the number of the route discovery, the average end-to-end delay, the number of collisions and the average throughput per route discovery, which is respectively compared with the ARA and AODV. [ABSTRACT FROM AUTHOR]

Collaborative logistics network about complex manufacturing is a dynamic open system. Its resource planning decision is easily influenced by stochastic uncertainty factors in network, so it presents the rigid sensitive and stable expiration phenomenon and belongs to the question that parameter status and probability distribution are unknown. In view of this completely uncertain question, the paper proposes a expected model of robust restraint, and carries uncertainty simulation on related parameter of network resource planning decision with Monte Carlo method. In comparison with optimization model which isn't constrained, the example results show that this model and method can actively control and weaken the negative influence of uncertain factors and increase the resource planning's stability and continuity. Finally, it provides a new angle of view for solving completely uncertain questions. [ABSTRACT FROM AUTHOR]