In this paper, a recurrent neural network for solving linear programming problems is presented that is simpler, intuitive and fast converging. To achieve optimality in accuracy and also in computational effort, an algorithm is presented. We investigate in this paper the MATLAB Simulink modeling and simulative verification of such a recurrent neural network. Modeling and simulative results subst...

Due to the multi-variable, nonlinear, large time delay and strong coupling features of the wastewater treatment process, a recurrent high-order neural network is used to model the key water quality parameters(Chemical Oxygen Demand, Biological Oxygen Demand, Suspended Solid and Ammonia Nitrogen) for the wastewater treatment process, and the neural network is trained by an filtering algorithm. O...

Learning long-term temporal dependencies with recurrent neural networks can be a difficult problem. It has recently been shown that a class of recurrent neural networks called NARX networks perform much better than conventional recurrent neural networks for learning certain simple long-term dependency problems. The intuitive explanation for this behavior is that the output memories of a NARX ne...

Learning long-term temporal dependencies with recurrent neural networks can be a difficult problem. It has recently been shown that a class of recurrent neural networks called NARX networks perform much better than conventional recurrent neural networks for learning certain simple long-term dependency problems. The intuitive explanation for this behavior is that the output memories of a NARX ne...

A trainable recurrent neural network, Simultaneous Recurrent Neural network, is proposed to address the scaling problem faced by neural network algorithms in static optimization. The proposed algorithm derives its computational power to address the scaling problem through its ability to "learn" compared to existing recurrent neural algorithms, which are not trainable. Recurrent backpropagation ...

Neural network has good nonlinear function approximation ability. It can be widely used to identify the model of controlled plant. In this chapter, the theories of modeling uncertain plant by using two kinds of neural networks: feed-forward neural network and recurrent neural network are introduced. And two adaptive control strategies for robotic tracking control are developed. One is recurrent...

In this paper, a recurrent fuzzy-neural network (RFNN) controller with neural network identifier in direct control model is designed to control the speed and exhaust temperature of the gas turbine in a combined cycle power plant. Since the turbine operation in combined cycle unit is considered, speed and exhaust temperature of the gas turbine should be simultaneously controlled by fuel command ...

This paper proposes a non-recurrent training algorithm, resilient propagation, for the Simultaneous Recurrent Neural network operating in relaxation-mode for computing high quality solutions of static optimization problems. Implementation details related to adaptation of the recurrent neural network weights through the non-recurrent training algorithm, resilient backpropagation, are formulated ...

Three recurrent neural networks are presented for computing the pseudoinverses of rank-deficient matrices. The first recurrent neural network has the dynamical equation similar to the one proposed earlier for matrix inversion and is capable of Moore–Penrose inversion under the condition of zero initial states. The second recurrent neural network consists of an array of neurons corresponding to ...

Forecasting a hydrologic time series has been one of the most complicated tasks owing to the wide range of data, the uncertainties in the parameters influencing the time series and also due to the non availability of adequate data. Recently Artificial Neural Networks (ANN) have become quite popular in time series forecasting in various fields. This paper demonstrates the use of ANN to forecast ...