control of robotic systems is an interesting subject due to their wide spectrum applications in medicine, aerospace and other industries. this paper proposes a novel continuous control mechanism for tracking problem of a 5-dof upper-limb exoskeleton robot. the proposed method is a combination of a recently developed robust integral of the sign of the error (rise) feedback and neural network (nn...

A neural network (NN) Modeling is presented based on biological and electrical behaviors. The proposed model is given with forward and reverse couplings with positive and negative signs. This model corresponds to the actual synaptic coupling of neurons. Neurons are connected mutually and allocated multiple accesses, total large numbers of neurons are included in the NN is the second advantage. ...

This paper presents the application of different Neural Network (NN) models for classifying the power system states as secure/insecure. Traditional method of security evaluation involves performing load flow and transient stability analysis for each contingency, making it infeasible for real time application. Pattern Recognition (PR) approach is recognized as an alternative tool. The NN models ...

This paper presents tools for the design of a neural network based adaptive output feedback controller for a class of nonlinear MIMO systems without zero dynamics. Each of the outputs is assumed to have relative degree less or equal to 2. Under the condition that the output functional dependence is unknown, a neural network based adaptive observer is designed to estimate the derivatives of the ...

This paper presents tools for the design of a neural network based adaptive output feedback controller for a class of nonlinear MIMO systems without zero dynamics. Each of the outputs is assumed to have relative degree less or equal to 2. Under the condition that the output functional dependence is unknown, a neural network based adaptive observer is designed to estimate the derivatives of the ...

A hybrid intelligent classiier is built for pattern classiication. It consists of a classiication and regression tree (CART), a genetic algorithm (GA) and a neural network (NN). CART extracts features of the patterns by setting up decision rules. Rule improvement by GA is explored. The rules act as a pre-processing layer of NN, a multi-class neural classiier, through which the most probable cla...

In this paper, it is shown that Multilayer perceptron Neural Network can elegantly perform nonlinear regression of transfer characteristic of electronic devices. After rigorous computer simulations authors develop the optimal MLP NN models, which elegantly perform such a nonlinear regression. Results show that the proposed optimal MLP NN models have optimal values of MSE (mean square error), r ...

This paper constructs fully parallel NN hardware realization of Artificial Neural Network (ANN) depends on the efficient execution of single neuron. Field Programmable Gate Array (FPGA) reconfigurable computing architecture is appropriated for hardware achievement of ANN. Numerous implementation of ANNs have been reported in scientific documents, trying to reduce Neural Networks NNs hardware ci...

The mixture of two already known soft computing techniques, like Genetic Algorithms and Neural Networks (NN) in Financial modeling, takes a new approach in the search for the best variables involving an Econometric model using a Neural Network. This new approach helps to recognize the importance of an economic variable among different variables regarding econometric modeling. A Genetic algorith...