TP381 97021063利用光纤Crossbar互连网络互连的二阵列光电混合多处理器系统的研究=Study of optoelectronichybrid multi-processor system with two arraysinterconnected by optical fiber crossbarinterconnection network[刊,中]/...TP381 97021063利用光纤Crossbar互连网络互连的二阵列光电混合多处理器系统的研究=Study of optoelectronichybrid multi-processor system with two arraysinterconnected by optical fiber crossbarinterconnection network[刊,中]/王忠祝,刘德森(中科院西安光机所.陕西,西安(710068))//光学展开更多
To extract the maximum power from a photovoltaic （PV） energy system, the real-time maximum power point （MPP） of the PV array must be tracked closely. The non-linear and time-variant characteristics of the PV array...To extract the maximum power from a photovoltaic （PV） energy system, the real-time maximum power point （MPP） of the PV array must be tracked closely. The non-linear and time-variant characteristics of the PV array and the non-linear and non-minimum phase characteristics of a boost converter make it difficult to track the MPP for traditional control strategies. We propose a fuzzy neural network controller （FNNC）, which combines the reasoning capability of fuzzy logical systems and the learning capability of neural networks, to track the MPP. With a derived learning algorithm, the parameters of the FNNC are updated adaptively. A gradient estimator based on a radial basis function neural network is developed to provide the reference information to the FNNC. Simulation results show that the proposed control algorithm provides much better tracking performance compared with the filzzy logic control algorithm.展开更多
文摘TP381 97021063利用光纤Crossbar互连网络互连的二阵列光电混合多处理器系统的研究=Study of optoelectronichybrid multi-processor system with two arraysinterconnected by optical fiber crossbarinterconnection network[刊,中]/王忠祝,刘德森(中科院西安光机所.陕西,西安(710068))//光学
基金Project (No. 20576071) supported by the National Natural Science Foundation of China
文摘To extract the maximum power from a photovoltaic （PV） energy system, the real-time maximum power point （MPP） of the PV array must be tracked closely. The non-linear and time-variant characteristics of the PV array and the non-linear and non-minimum phase characteristics of a boost converter make it difficult to track the MPP for traditional control strategies. We propose a fuzzy neural network controller （FNNC）, which combines the reasoning capability of fuzzy logical systems and the learning capability of neural networks, to track the MPP. With a derived learning algorithm, the parameters of the FNNC are updated adaptively. A gradient estimator based on a radial basis function neural network is developed to provide the reference information to the FNNC. Simulation results show that the proposed control algorithm provides much better tracking performance compared with the filzzy logic control algorithm.
