predictive quantitative structure–activity relationship was performed on the novel 4-oxo-1,4-dihydroquinoline and 4-oxo-4h-pyrido[1,2-a]pyrimidine derivatives to explore relationship between the structure of synthesized compounds and their anti-hiv-1 activities. in this way, the suitable set of the molecular descriptors was calculated and the important descriptors using the variable selections ...

The paper presents an investigation in to the effect of neural network predictive control of UPFC on the transient stability performance of a multimachine power system. The proposed controller consists of a neural network model of the test system. This model is used to predict the future control inputs using the damped Gauss-Newton method which employs ‘backtracking’ as the line search method f...

the main purpose of this paper is prediction of tse corporate financial bankruptcy using artificial neural networks. the mean values of key ratios reported in past bankruptcy studies were selected for neural network inputs (working capital to total assets, net income to total assets, total debt to total assets, current assets to current liabilities, quick assets to current liabilities). the neu...

In this paper an accurate nonlinear model of induction motor using an artificial neural network (ANN) is given. This modeling technique is done by using the data from the system inputs/outputs information without requiring the knowledge about machine parameters. The ANN training is carried out off-line using the Levenberg-Marquardt algorithm. Then, the proposed neural network model is used as p...

In this paper, a deep neural network (DNN)-based nonlinear model predictive controller (NMPC) is demonstrated using real-time experimental implementation. First, the emissions and performance of 4.5-liter 4-cylinder Cummins diesel engine are modeled DNN with seven hidden layers 24,148 learnable parameters created by stacking six Fully Connected one long-short term memory (LSTM) layer. This then...

In recent years, there has been an expansive growth in the study and implementation of neural networks over a spectrum of research domains. Neural network based Predictive control is recognized as an efficient methodology to address difficult control problems. The NARMA model is an exact representation of the input-output behaviour of finite dimensional non-linear discrete time dynamical system...

laguerre function has many advantages such as good approximation capability for different systems, low computational complexity and the facility of on-line parameter identification. therefore, it is widely adopted for complex industrial process control. in this work, laguerre function based adaptive model predictive control algorithm (ampc) was implemented to control continuous stirred tank rea...

A capital issue in roll-gap control for rolling mill plants is the difficulty to measure the output thickness without including time delays in the control loop. Time delays are a consequence of the possible locations for the output thickness sensor which is usually located some distance away from the roll gap. In this work, a new model-based predictive control law is proposed. The new scheme is...

This paper is concerned with neural-learning control of nonlinear dynamical systems. A variable neural network is introduced for approximating unknown nonlinearities of dynamical systems. Based on variable neural networks, adaptive neural control and predictive neural control schemes are studied. In the adaptive neural control scheme, the weight-learning laws and adaptive controller developed u...