Intelligent control decision integrating fuzziness and randomness for automatic management of cash flow.

Automatic management of cash flow from the perspective of cybernetics decisions can improve work efficiency and accuracy of cash flow management. Disadvantage of traditional fuzzy control method is that it only expresses fuzziness and ignores randomness. The automatic management of cash flow involves variables representing the fuzziness and randomness of human cognition which need new calculation methods to solve. Based on fuzzy control this paper proposes a cloud set control decision method for cash flow management. Cloud set and its I operation and P operation are described. Methods are studied including observation variables and control variables, fuzziness of observation variables and control variables, description of rules, and cloud reasoning based on cloud set. The method is applied successfully in automatic management of cash flow in which control amount of expenditure intensity is -2.285. It is shown that this method can effectively obtain reasonable control quantities considering fuzzy and random properties by the comparison with fuzzy control method. The method for automatic management of cash flow proposed has greater objectivity and effectiveness for the integration of fuzzy and randomness representing human cognition and decision.

Conflict management based on belief function entropy in sensor fusion.

Wireless sensor network plays an important role in intelligent navigation. It incorporates a group of sensors to overcome the limitation of single detection system. Dempster-Shafer evidence theory can combine the sensor data of the wireless sensor network by data fusion, which contributes to the improvement of accuracy and reliability of the detection system. However, due to different sources of sensors, there may be conflict among the sensor data under uncertain environment. Thus, this paper proposes a new method combining Deng entropy and evidence distance to address the issue. First, Deng entropy is adopted to measure the uncertain information. Then, evidence distance is applied to measure the conflict degree. The new method can cope with conflict effectually and improve the accuracy and reliability of the detection system. An example is illustrated to show the efficiency of the new method and the result is compared with that of the existing methods.

Modeling Sensor Reliability in Fault Diagnosis Based on Evidence Theory.

Sensor data fusion plays an important role in fault diagnosis. Dempster-Shafer (D-R) evidence theory is widely used in fault diagnosis, since it is efficient to combine evidence from different sensors. However, under the situation where the evidence highly conflicts, it may obtain a counterintuitive result. To address the issue, a new method is proposed in this paper. Not only the statistic sensor reliability, but also the dynamic sensor reliability are taken into consideration. The evidence distance function and the belief entropy are combined to obtain the dynamic reliability of each sensor report. A weighted averaging method is adopted to modify the conflict evidence by assigning different weights to evidence according to sensor reliability. The proposed method has better performance in conflict management and fault diagnosis due to the fact that the information volume of each sensor report is taken into consideration. An application in fault diagnosis based on sensor fusion is illustrated to show the efficiency of the proposed method. The results show that the proposed method improves the accuracy of fault diagnosis from 81.19% to 89.48% compared to the existing methods.