This paper proposes a diagonal recurrent neural network (DRNN) based identification
model for approximating the unknown dynamics of the nonlinear plants. The proposed …

In this paper, a comparative study is performed to test the approximation ability of different
neural network structures. It involves three neural networks multilayer feedforward neural …

This paper proposes an Externally Recurrent Neural Network (ERNN) for approximating the
unknown dynamics of complex nonlinear systems and time series prediction. The proposed …

The paper proposed to apply a hierarchical fuzzy-neural multi-model and Takagi–Sugeno (T–
S) rules with recurrent neural procedural consequent part for systems identification, states …

The application of neural networks can present some limitations for the control of strongly
nonlinear systems. In this paper, a new control scheme based on a neural multicontroller …

This paper addresses the problem of predicting the sales by developing two sales
forecasting models based on multi-layered perceptron (MLP) and radial basis function …

In the paper it is proposed a new recurrent fuzzy-neural multi-model (FNMM) identifier
applied for decentralized identification of a distributed parameter anaerobic wastewater …

На основе вещественного интерполяционного метода разработана методика
определения момента инерции в электроприводе. Расчет осуществляется по …

The paper proposed to use recurrent Fuzzy-Neural Multi-Model (FNMM) identifier for
decentralized identification of a distributed parameter anaerobic wastewater treatment …

Abstract A Recurrent Trainable Neural Network (RTNN) with a two layer canonical
architecture and a dynamic Backpropagation learning method are applied for local …