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The concepts of sets and approximate reasoning within extended fuzzy logic (FLe) provide a systematic procedure for transforming unprecisiated knowledge into a nonlinear mapping over what we define here as f -sets. An f -set differs from a fuzzy set in that it is associated with the restriction of validity in addition to that of possibility. Therefore, by f -set, we can simultaneously deal with two different types of uncertainties: one that is related to ill-known objects represented by incomplete information—information with its one or more aspects being imprecise/vague/partial/nonspecific/undetermined—and another that is related to truth values considering gradualness. Here, we define new concepts of $\vartheta $ -cuts and $\alpha \vartheta $ -cuts, introduce the f -extension principle, and consider arithmetic computations within FLe. We then address other aspects of the proposed FLe system such as fuzzification and validification operations in input processing stage, set-conversion and defuzzification in output processing stage, and inferencing. In fact, in this paper, we intend to develop FLe theoretically and practically from the stands of sets and systems to extend the concept of approximate reasoning. As a consequence of this development, we assert that considering the validity degree of methods and information can lead to more reasonable and trustworthy results through capturing more uncertainty.

An algorithm is proposed for scheduling dependent tasks in time-varying heterogeneous multiprocessor systems, in which computational power and links between processors are allowed to change over time. Link contention is considered in the multiprocessor scheduling problem. A linear switching-state space-modeling paradigm is introduced to enable theoretical analysis from a system engineering perspective. Theoretical analysis of this model shows its robustness against changes in processing power and link failure. The proposed algorithm uses a fuzzy decision-making procedure to handle changes in the multiprocessor system. The efficiency of the proposed algorithm is illustrated by several random experiments and comparison against a recent benchmark approach. The results show up to 18% average improvement in makespan, especially for larger scale systems.

Earlier works have reported that reward shaping accelerates the convergence of reinforcement learning algorithms. It also helps to make better use of existing information. In this article we propose the use to modify Q-learning in multiagent systems by the use of reward shaping depending on agent state regarding other agents. We study this method with different choices, which indicate different effects of this method on the maze problem. The results indicate the directional search, reduces the number of steps to reach the target in the proposed modified approach if appropriate parameters are utilized.

Appropriate sensor placement can strongly influence the control performance of structures. However, there is not yet a systematic method for sensor placement. In this paper, a general method based on a proposed constrained GA is suggested to optimally place sensors in structures. The optimal placement scheme is general for passive, active and semi-active controls and it does not depend on the control strategy and nonlinear dynamics of the control system. Due to low cost, high reliability, control effectiveness as well as installation simplicity, acceleration type sensors are considered. The proposed method is applicable to new or existing buildings, as the accelerometer placement is trivial. The efficiency of proposed method is evaluated on the benchmark building for placement of 5 and 3 sensors. The results show that the performance of control system with 5 and even 3 optimally placed sensors is at least 8% better than the original benchmark building design with 5 sensors. Generally, the proposed method is a simple and efficient practical approach to achieve improved performance using the fewest number of instruments.

The original version of this article unfortunately contained a mistake. The affiliation of the first author was incorrect. The correct affiliation is: Department of Computer Engineering, Quchan Branch, Islamic Azad University, Quchan, Iran. Journal of Zhejiang University-SCIENCE C (Computers & Electronics) ISSN 1869-1951 (Print); ISSN 1869-196X (Online) www.zju.edu.cn/jzus; www.springerlink.com E-mail: jzus@zju.edu.cn