In the framework of nonlinear process modeling, we propose training algorithms for feedback wavelet networks used as nonlinear dynamic models. An original initialization procedure...
Recurrent neural networks serve as black-box models for nonlinear dynamical systems identification and time series prediction. Training of recurrent networks typically minimizes t...
: This paper presents a new path-tracking scheme for a car-like mobile robot based on neural predictive control. A multi-layer back-propagation neural network is employed to model ...
Goal recognition in digital games involves inferring players’ goals from observed sequences of low-level player actions. Goal recognition models support player-adaptive digital ...
Eun Y. Ha, Jonathan P. Rowe, Bradford W. Mott, Jam...
— A single biological neuron is able to perform complex computations that are highly nonlinear in nature, adaptive, and superior to the perceptron model. A neuron is essentially ...