a fuzzy-based method for evaluating the trustworthiness of software processes

University of Paderborn; Fraunhofer Inst. Exp. Softw. Eng. (IESE); Chinese Academy of Science (ISCAS)

Prediction of marine sediment corrosion by fuzzy clustering analysis

Along with the development of marine industries, especially marine petroleum exploitation, more and more pipelines are buried in the marine sediment. It is necessary and useful to know the corrosion environment and corrosiveness of marine sediment. In this paper, field corrosion environmental factors were investigated in Liaodong Bay marine sediment containing sulfate-reducing bacteria (SRB) and corrosion rate of steel in the partly sediment specimens were determined by the transplanting burying method. Based on the data, the fuzzy clustering analysis (FCA) was applied to evaluate and predict the corrosiveness of marine sediment. On that basis, the influence factors of corrosion damage were discussed.

An application of the analytic hierarchy process and fuzzy logic inference in a decision support system for forage selection

Forage selection plays a prominent role in the process of returning cultivated lands back into grasslands. The conventional method of selecting forage species can only provide attempts for problem-solving without considering the relationships among the decision factors globally. Therefore, this study is dedicated to developing a decision support system to help farmers correctly select suitable forage species for the target sites. After collecting data through a field study, we developed this decision support system. It consists of three steps: (1) the analytic hierarchy process (AHP), (2) weights determination, and (3) decision making. In the first step, six factors influencing forage growth were selected by reviewing the related references and by interviewing experts. Then a fuzzy matrix was devised to determine the weight of each factor in the second step. Finally, a gradual alternative decision support system was created to help farmers choose suitable forage species for their lands in the third step. The results showed that the AHP and fuzzy logic are useful for forage selection decision making, and the proposed system can provide accurate results in a certain area (Gansu Province) of China.

具有不完全微分的Fuzzy-GA PID控制器及其在智能仿生人工腿中的应用

以模糊推理和遗传算法为基础，提出了一种新的具有不完全微分的最优PID控制器的设计方法，该控制器由离线和在线两部分组成，在离线部分，以系统响应的超调量、上升时间以及调整时间为性能指标，利用遗传算法搜索出一组最优的PID参数Kp^*、Ti^*和Td^*，作为在线部分调整的初始值，在在线部分，一个专用的PID参数优化程序以离线部分获得Kp^*、Ti^*和Td^*为基础，根据系统当前的误差e和误差变化率e^.，通过一个模糊推理系统在线调整系统瞬态响应的PID参数，以确保系统的响应具有最优的动态和稳态性能．该控制器已被用来控制由作者设计的智能仿生人工腿中的执行电机．计算机仿真结果表明，该控制器具有良好的控制性能和鲁棒性能。

Optimal Fuzzy PID Controller with Incomplete Derivation and Its Simulation Research on Application of Intelligent Artificial Legs

提出了一种新的最优模糊PID控制器，它由两部分组成，即在线模糊推理机构和带有不完全微分的常规PID控制器，在模糊推理机构中，引入了三个可调节因子xp,xi和xd，其作用是进一步修改和优化模糊推理的结果，以使控制器对一个给定对象具有最优的控制效果，可调节因子的最优值采用ITAE准则及Nelder和Mead提出的柔性多面体最优搜索算法加以确定，这种PID控制器被用来控制由作者设计的智能人工腿中的一个直流电机，仿真结果表明该控制器的设计是非常有效的，它可被用于控制各种不同的对象和过程。

Fuzzy层次神经网络

本文提出和归纳了Ｆｕｚｚｙ前馈神经网络模型，并提出了Ｆｕｚｚｙ前馈神经网络的反向传播学习算法，模拟结果表明利用ＦｕｚｚｙＢＰ学习算法训练的Ｆｕｚｚｙ前馈神经网络具有较好的非逻辑归纳能力和Ｆｕｚｚｙ规则表达能力。

Fuzzy-Rough Sets Assisted Attribute Selection

R. Jensen and Q. Shen. Fuzzy-Rough Sets Assisted Attribute Selection. IEEE Transactions on Fuzzy Systems, vol. 15, no. 1, pp. 73-89, 2007.

Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough-Based Approaches

R. Jensen and Q. Shen. Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough Based Approaches. IEEE Transactions on Knowledge and Data Engineering, 16(12): 1457-1471. 2004.

Towards Fuzzy Compositional Modelling

X. Fu, Q. Shen and R. Zhao. 'Towards fuzzy compositional modelling,' In Proceedings of the 16th International Conference on Fuzzy Systems, 2007, pp. 1233-1238. Sponsorship: EPSRC

Knowledge representation for fuzzy model composition

X. Fu and Q. Shen. 'Knowledge representation for fuzzy model composition', in Proceedings of the 21st International Workshop on Qualitative Reasoning, 2007, pp. 47-54. Sponsorship: EPSRC

Fuzzy-Rough Data Reduction with Ant Colony Optimization.

R. Jensen and Q. Shen, 'Fuzzy-Rough Data Reduction with Ant Colony Optimization,' Fuzzy Sets and Systems, vol. 149, no. 1, pp. 5-20, 2005.

Fuzzy-Rough Attribute Reduction with Application to Web Categorization.

R. Jensen and Q. Shen, 'Fuzzy-Rough Attribute Reduction with Application to Web Categorization,' Fuzzy Sets and Systems, vol. 141, no. 3, pp. 469-485, 2004.

Selecting Informative Features with Fuzzy-Rough Sets and its Application for Complex Systems Monitoring.

Q. Shen and R. Jensen, 'Selecting Informative Features with Fuzzy-Rough Sets and its Application for Complex Systems Monitoring,' Pattern Recognition, vol. 37, no. 7, pp. 1351-1363, 2004.

Survey of Rough and Fuzzy Hybridization.

P. Lingras and R. Jensen, 'Survey of Rough and Fuzzy Hybridization,' Proceedings of the 16th International Conference on Fuzzy Systems (FUZZ-IEEE'07), pp. 125-130, 2007.