This paper proposes a neural controller to guide a nonholonomic mobile robot during trajectory tracking. The structure of the controller used consists of two models that describe the kinematical mobile robot system. These models are modified Elman neural networks (MENN) and feed forward multi-layer perceptron (MLP). The modified Elman neural networks model is trained with two stages; off-line a...

artificial neural networks have the advantages such as learning, adaptation, fault-tolerance, parallelism and generalization. this paper mainly intends to offer a novel method for finding a solution of a fuzzy equation that supposedly has a real solution. for this scope, we applied an architecture of fuzzy neural networks such that the corresponding connection weights are real numbers. the sugg...

This paper discusses the concurrent design of a feedforward time-delay filter and a linear state feed-back controller. The optimization is carried out using a quadratic cost functional which weights both, disturbance rejection and control tracking performance. A minimax optimization scheme is employed to achieve robustness with respect to parameter deviations. Analytical gradients are provided ...

In this study, a time-dependent 2D axisymmetric model of a multilayer hollow fiber composite membrane for gas separation is proposed. In spite of the common multilayer membranes, which a dense layer coated on a porous support layer and subjected into the feed stream, here, the porous support is exposed to the feed gas. In this regard, the governing equations of species transport are developed f...

Abstract Logic-based machine learning aims to learn general, interpretable knowledge in a data-efficient manner. However, labelled data must be specified structured logical form. To address this limitation, we propose neural-symbolic framework, called Feed-Forward Neural-Symbolic Learner (FFNSL) , that integrates logic-based system capable of from noisy examples, with neural networks, order uns...