Search

An adaptive neuro-fuzzy based method for analyzing odour generation factors to the perception of livestock farm odour was proposed. In this approach, the parameters associated with a given membership function could be tuned so as to tailor the membership functions to the input/output data in order to account for these types of variations in the data values. A multi-factor livestock farm odour model was developed, and both numeric factors and linguistic factors were considered. The proposed method was tested with a livestock farm odour database. The results demonstrated the effectiveness of the proposed approach in comparison to a typical neural network. [ABSTRACT FROM AUTHOR]

There exists a challenge, i.e., to diagnose failures of such a complex system that has the following characters: (1) it has a causality loop structure; (2) system observed variables are discrete, or continuous, or mixed; and (3) system has time lag, i.e., it has delay of fault effect. For the task, this paper proposes a fault diagnosis prototype system based on causality diagram, and describes the key technologies used. The proposed prototype system comprises datum collection subsystem, model design subsystem, fault diagnosis subsystem, and diagnosis result display subsystem. In this system, the fault knowledge related fault diagnosis is represented in a fault influence propagation diagram (FIPD), which results from a causality diagram. The fault knowledge related delay of fault effect is represented in a 2-time-slice causality diagram (2-TSCD), which is a time stochastic model extended from a FIPD. The method of find fault modes in a FIPD or a 2-TSCD is presented. The proposed prototype system has flexible fault knowledge representation, rapid diagnosis process, and well practicability. [ABSTRACT FROM AUTHOR]