A hybrid approach for fuzzy system design based on clustering and a kind of neurofuzzy networks is proposed. An unsupervised clustering technique is firstly used to determine the number of if-then fuzzy rules and gene...A hybrid approach for fuzzy system design based on clustering and a kind of neurofuzzy networks is proposed. An unsupervised clustering technique is firstly used to determine the number of if-then fuzzy rules and generate an initial fuzzy rule base from the given input-output data. Then, a class of neurofuzzy networks is constructed and its weights are tuned so that the obtained fuzzy rule base has a high accuracy. Finally, two examples of function approximation problems are given to illustrate the effectiveness of the proposed approach.展开更多
An online fault diagnostic scheme for nonlinear systems based on neurofuzzy networks is proposed in this paper. The scheme involves two stages. In the first stage, the nonlinear system is approximated by a neurofuzzy ...An online fault diagnostic scheme for nonlinear systems based on neurofuzzy networks is proposed in this paper. The scheme involves two stages. In the first stage, the nonlinear system is approximated by a neurofuzzy network, which is trained offline from data obtained during the normal operation of the system. In the second stage, residual is generated online from this network and is modelled by another neurofuzzy network trained online. Fuzzy rules are extracted from this network, and are compared with those in the fault database obtained under different faulty operations, from which faults are diagnosed. The performance of the proposed intelligent fault scheme is illustrated using a two-tank water level control system under different faulty conditions .展开更多
基金This project was supported by the National Natural Science Foundation of China (60141002).
文摘A hybrid approach for fuzzy system design based on clustering and a kind of neurofuzzy networks is proposed. An unsupervised clustering technique is firstly used to determine the number of if-then fuzzy rules and generate an initial fuzzy rule base from the given input-output data. Then, a class of neurofuzzy networks is constructed and its weights are tuned so that the obtained fuzzy rule base has a high accuracy. Finally, two examples of function approximation problems are given to illustrate the effectiveness of the proposed approach.
基金This paper was presented at the 25th Chinese Control Conference and was supported by the HKSAR RGC Grant (HKU 7050/02E).
文摘An online fault diagnostic scheme for nonlinear systems based on neurofuzzy networks is proposed in this paper. The scheme involves two stages. In the first stage, the nonlinear system is approximated by a neurofuzzy network, which is trained offline from data obtained during the normal operation of the system. In the second stage, residual is generated online from this network and is modelled by another neurofuzzy network trained online. Fuzzy rules are extracted from this network, and are compared with those in the fault database obtained under different faulty operations, from which faults are diagnosed. The performance of the proposed intelligent fault scheme is illustrated using a two-tank water level control system under different faulty conditions .
