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When rule base is sparse, we cannot get any reasoning result by traditional CRI method, however interpolative reasoning methods can tackle this problem. After analyzing the interpolative reasoning method based on center of gravity by Zhiheng Huang and Qiang Shen, this paper presents a generalized fuzzy interpolative method based on center of gravity (COG). This method allows the conditions appearing in the antecedent part and the consequence of the rules to be represented not only by triangular fuzzy numbers but also by trapezoidal fuzzy numbers; moreover it can keep the convexity of the inferential consequence. In this process, scale and move rate transformation operations are used to support such reasoning.

Traditional TCP congestion control has poor performance in high-speed networks because of its slow response with large congestion windows. HSTCP has been proposed as one of the possible ways to better utilize the networks with high bandwidth-delay products. Experiments revealed that HSTCP can use the bandwidth more sufficient than current TCP, but it has severe RTT unfairness. In this paper, we first investigate the RTT unfairness through mathematical analysis and simulation studies, and then we add a fair factor to the HSTCP to decrease the difference of window increase caused by different RTT. Experiments show that the modified algorithm can guarantee the bandwidth fairness of HSTCP flows and reduce the packets loss rate.

To improve the performance of the existing genetic algorithms for job shop scheduling problem and speed up searching for optimal scheduling solution, this paper analyzes the difficulty and characteristics of the operation-based coding and designs a new crossover, which is based on the job. As illustrative numerical examples, both 6times6 and 10 times 10 job-shop scheduling problems are considered. Through the comparative simulations with position-based crossover, the feasibility and effectiveness of the proposed crossover are demonstrated.