feedforward 
Approximation capabilities of multilayer feedforward regular fuzzy neural networks


Fourlayer feedforward regular fuzzy neural networks are constructed.


This includes feedback control algorithm and feedforward control algorithm.


The feedback controller improves system tracking performance and suppresses load and mechanical disturbance while the feedforward controller compensates phase hysteresis introduced by feedback control.


A method is presented for synthesis of optimal sampleddata tracking systems with two controllers, one of which is incorporated into the closed loop, while another is placed in feedforward chain.


On Stable Estimation of the Parameters of Feedforward Neural Networks in Dealing with Biological Objects


The results obtained earlier are extended to the cases of a nonlinear regression and a feedforward neural network with one hidden layer.


An explanation is given for the fact that, at low signaltonoise ratios, a systematic feedback encoder results in fewer decoding bit errors than a nonsystematic feedforward encoder for the same tailbiting code.


It is particularly shown that any two orthogonal states can be perfectly discriminated using only linear optics, photon counting, coherent ancillary states, and feedforward.


Such a system possesses some universal approximation capabilities, that is, the corresponding three layer feedforward fuzzy neural networks can be universal approximators to the continuously increasing fuzzy functions.


Such a system is a multilayer feedforward neural network, which can be a universal approximator with maximum norm.


For the nearly exponential type of feedforward neural networks (neFNNs), it is revealed the essential order of their approximation.


Application of optimization technology for ratio of air to fuel combining feedforward with feedback in heating furnace


Combining the technique of fuzzy control with calorific value of feedforward and oxygen concentration of waste gas feedback, the optimization model for ratio of air to fuel is developed and utilized in practice.


Bent functions are also introduced into the studies of linear approximation and entropy immunity for feedforward networks.


A multilayer feedforward neural network model for visual motion perception


In this paper a multilayered feedforward neural network model for perception of visual motion is presented.


The application of multilayer feedforward network for image segmentation


The multilayer feedforward network is used for image segmentation.


The experiment shows that the image segmentation can get better result from using the multilayer feedforward network.

