Based on the operation data from a certain wastewater treatment plant(WWTP) in northeast China, the models of back propagation neural network(BP NN) and radial basis function neural network(RBF NN) have been designed ...Based on the operation data from a certain wastewater treatment plant(WWTP) in northeast China, the models of back propagation neural network(BP NN) and radial basis function neural network(RBF NN) have been designed respectively and the ability of convergence and generalization has been analyzed separately. As for BP NN, the effects of numbers of layers and nodes have been studied; as for RBF NN, the influences of the number of nodes and the RBF′s width have been studied. It is concluded that BP NN has converged much slowly in comparison with RBF NN. The conclusion that the RBF NN is suitable for modeling activated sludge system has been drawn. An automatically optimum design program for RBF NN has been developed, through which the RBF NN model of traditional activated sludge system has been established.展开更多
Back propagation (BP) algorithm is a very useful algorithm in many areas, but its leaning process is a very complicated non linear convergence process, in which, chaos often happens, and slow convergence speed and loc...Back propagation (BP) algorithm is a very useful algorithm in many areas, but its leaning process is a very complicated non linear convergence process, in which, chaos often happens, and slow convergence speed and local least often make it difficult for the non experts to use it widely, and an improved BP (IBP) algorithm is therefore suggested to expedite the convergence speed. The algorithm can judge local least and take some steps automatically to jump out from the local least. Furthermore, this algorithm introduces the expert knowledge base. An IBP based agile and current neural network (NN) constructed tool is designed. An initial NN can be constructed automatically using an expert knowledge base. And an Aitken’s Δ 2 process method is used to expedite the convergent speed for NN. Besides, the method of changing the parameter of Sigmoid function and increasing the hidden node is used to bring surge for NN to jump out from the local展开更多
文摘Based on the operation data from a certain wastewater treatment plant(WWTP) in northeast China, the models of back propagation neural network(BP NN) and radial basis function neural network(RBF NN) have been designed respectively and the ability of convergence and generalization has been analyzed separately. As for BP NN, the effects of numbers of layers and nodes have been studied; as for RBF NN, the influences of the number of nodes and the RBF′s width have been studied. It is concluded that BP NN has converged much slowly in comparison with RBF NN. The conclusion that the RBF NN is suitable for modeling activated sludge system has been drawn. An automatically optimum design program for RBF NN has been developed, through which the RBF NN model of traditional activated sludge system has been established.
文摘Back propagation (BP) algorithm is a very useful algorithm in many areas, but its leaning process is a very complicated non linear convergence process, in which, chaos often happens, and slow convergence speed and local least often make it difficult for the non experts to use it widely, and an improved BP (IBP) algorithm is therefore suggested to expedite the convergence speed. The algorithm can judge local least and take some steps automatically to jump out from the local least. Furthermore, this algorithm introduces the expert knowledge base. An IBP based agile and current neural network (NN) constructed tool is designed. An initial NN can be constructed automatically using an expert knowledge base. And an Aitken’s Δ 2 process method is used to expedite the convergent speed for NN. Besides, the method of changing the parameter of Sigmoid function and increasing the hidden node is used to bring surge for NN to jump out from the local
