A new type of recurrent neural network is discussed, which provides the potential for modelling unknown nonlinear systems. The proposed network is a generalization of the network described by Elman, which has three la...A new type of recurrent neural network is discussed, which provides the potential for modelling unknown nonlinear systems. The proposed network is a generalization of the network described by Elman, which has three layers including the input layer, the hidden layer and the output layer. The input layer is composed of two different groups of neurons, the group of external input neurons and the group of the internal context neurons. Since arbitrary connections can be allowed from the hidden layer to the context layer, the modified Elman network has more memory space to represent dynamic systems than the Elman network. In addition, it is proved that the proposed network with appropriate neurons in the context layer can approximate the trajectory of a given dynamical system for any fixed finite length of time. The dynamic backpropagation algorithm is used to estimate the weights of both the feedforward and feedback connections. The methods have been successfully applied to the modelling of nonlinear plants.展开更多
The paper presents experimental and theoretical studies of non-thermal plasma assisted reforming of liquid ethanol into hydrogen-rich syngas in dynamic plasma-liquid systems （PLS） using electric DC and pulsed discha...The paper presents experimental and theoretical studies of non-thermal plasma assisted reforming of liquid ethanol into hydrogen-rich syngas in dynamic plasma-liquid systems （PLS） using electric DC and pulsed discharges in a gas channel with liquid wall （DGCLW） and DC discharge in a reverse vortex gas flow of Tornado type with ＂liquid＂ electrode （TORNADO-LE）. Results of experiments show the energy efficiency of plasma-chemical conversion of ethanol in studied systems. Results of model calculations explain the kinetic mechanism of non-equilibrium plasma-chemical transformations in different conditions. The proposed technique of plasma-fuel reforming can be used in alternative biofuels combustion technologies in advanced diesel engines and power plants.展开更多
文摘A new type of recurrent neural network is discussed, which provides the potential for modelling unknown nonlinear systems. The proposed network is a generalization of the network described by Elman, which has three layers including the input layer, the hidden layer and the output layer. The input layer is composed of two different groups of neurons, the group of external input neurons and the group of the internal context neurons. Since arbitrary connections can be allowed from the hidden layer to the context layer, the modified Elman network has more memory space to represent dynamic systems than the Elman network. In addition, it is proved that the proposed network with appropriate neurons in the context layer can approximate the trajectory of a given dynamical system for any fixed finite length of time. The dynamic backpropagation algorithm is used to estimate the weights of both the feedforward and feedback connections. The methods have been successfully applied to the modelling of nonlinear plants.
文摘The paper presents experimental and theoretical studies of non-thermal plasma assisted reforming of liquid ethanol into hydrogen-rich syngas in dynamic plasma-liquid systems （PLS） using electric DC and pulsed discharges in a gas channel with liquid wall （DGCLW） and DC discharge in a reverse vortex gas flow of Tornado type with ＂liquid＂ electrode （TORNADO-LE）. Results of experiments show the energy efficiency of plasma-chemical conversion of ethanol in studied systems. Results of model calculations explain the kinetic mechanism of non-equilibrium plasma-chemical transformations in different conditions. The proposed technique of plasma-fuel reforming can be used in alternative biofuels combustion technologies in advanced diesel engines and power plants.
