In this paper, a second order plant is considered for identification using NN-predictive control technique. The neural network based predictive controller is configured based on MATLAB 7.0. This neural network controller uses a neural network model of plant. It predicts the future performance of the actual plant. The controller uses to calculate the control input. The control input will optimiz...

This article presents numerical studies on semi-active seismic response control of structures equipped with Magneto-Rheological (MR) dampers. A multi-layer artificial neural network (ANN) was employed to mitigate the influence of time delay, This ANN was trained using data from the El-Centro earthquake. The inputs of ANN are the seismic responses of the structure in the current step, and the ou...

• “An Efficient Training Technique for a Neural-Network Controller for Seismically Excited Structures,” (reference [54]) by Liut et al. • “An overview of Some Non-Traditional Neural-Network Training Strategies for Seismic Response Suppression of Building Structures,” (reference [56]) by Liut et al. • “A Modified Gradient-Search Training Technique for Neural-Network Structural Control,” (referen...

 Abstract—several neural networks controllers for robotics manipulators have been developed during the last decades due to their capability to learn the dynamics properties and the improvements in the global stability of the system. In this paper, two control and identification schemes for a two links robotic manipulator implementing neural networks are presented. A multilayer feedforward neu...

The field of neural networks has seen significant advances in recent years with the development deep and convolutional networks. Although many current works address real-valued models, studies reveal that hypercomplex-valued parameters can better capture, generalize, represent complexity multidimensional data. This paper explores quaternion-valued network application for a pattern recognition t...

A new adaptive multiple-controller is proposed incorporating a neural network based Generalized Learning Model (GLM). The GLM assumes that the unknown complex plant is represented by an equivalent stochastic model consisting of a linear time-varying sub-model plus a Radial Basis Function (RBF) neural-network based learning sub-model . The proposed non-linear multiple-controller methodology prov...

the control of fluidized-bed operations processes is still one of the major areas of research due to the complexity of the process and the inherent nonlinearity and varying dynamics involved in its operation. there are varieties of problems in chemical engineering that can be formulated as nonlinear programming (nlps). the quality of the developed solution significantly affects the performance ...

This paper presents a strategy for controlling inverter-interfaced DERs within a microgrid using an artificial neural network. The neural network implements a dynamic programming algorithm and is trained with a new Levenberg-Marquardt backpropagation algorithm. Hardware experiments were conducted to evaluate the performance of the neural network vector control method. They showed that the neura...

In the paper a new structure of Multi-Layer Perceptron, able to deal with quaternion-valued signal, is proposed. A learning algorithm for the proposed Quaternion MLP (QMLP) is also derived. Such a neural network allows to interpolate functions of quaternion variable with a smaller number of connections with respect to the corresponding real valued MLP. INTRODUCTION In the last few years, neural...