The main objective of this paper is to propose a new friction compensation mechanism applied to robotic actuators, and to confirm it through experimental results. Friction is a phenomenon that changes with time and with actuator’s operational conditions. To deal with these parameters variations, it is proposed a neuro-fuzzy algorithm for friction identification and compensation. A Neural Networ...

Hardware realization is very important when considering wider applications of neural networks (NNs). In particular, hardware NNs with a learning ability are intriguing. In these networks, the learning scheme is of much interest, with the backpropagation method being widely used. A gradient type of learning rule is not easy to realize in an electronic system, since calculation of the gradients f...

This research is defined a new neural network (NN) that depends upon positive integer parameter using the multivariate square rational Bernstein polynomials. Some theorems for this are proved, such as pointwise and uniform approximation theorems. Firstly, absolute moment function belongs to Lipschitz space estimate order of NN. Secondly, some numerical applications NN given by taking two test f...

Several researchers have successfully used Artificial Neural Networks (NN) to process natural languages. The way in which the neurons of these networks were connected to each other was based on notions of how it was that the NN should attack the problem at hand. How to connect neurons in order to guarantee optimal performance for any one task is still an open question. At the same time, researc...

A new robust learning controller for simultaneous position/force control of constrained uncertain rigid-link electrically-driven (RLED) manipulators is presented. In the controller, a robust nonlinear control design and the direct adaptive neural networks (NN) technique are integrated together. Firstly, NN devices are used to adaptively learn those RLED manipulator’s structured/unstructured unc...

in this paper, a sensitivity analysis of artificial neural networks (nns) is presented and employed for estimating the patch load resistance of plate girders subjected to patch loading. to evaluate the accuracy of the proposed nn model, the results are compared with the previously proposed empirical models, so that we can estimate the resistance of plate girders subjected to patch loading. the ...

Neural networks (NN) architectures can be effectively used to classify, forecast and recognize quantity of interest in, e.g., computer vision, machine translation, finance, etc. Concerning the financial framework, forecasting procedures are often used as a part of the decision making process in both trading and portfolio strategy optimization. Unfortunately training a NN is in general a challen...

We present a methodology combining neural networks with physical principle constraints in the form of partial differential equations (PDEs). The approach allows to train while respecting PDEs as strong constraint optimisation apposed making them part loss function. resulting models are discretised space by finite element method (FEM). applies both stationary and transient well linear/nonlinear ...

Neural network (NN)-based modeling often involves trying multiple networks with diierent architectures and training parameters in order to achieve acceptable model accuracy. Typically, one of the trained NNs is chosen as best, while the rest are discarded. Hashem and Schmeiser 25] proposed using optimal linear combinations of a number of trained neural networks instead of using a single best ne...