The paper is devoted to development of control algorithms for nonlinear parametrically uncertain systems. Original system dynamics is approximated by a set of local NARX models combined by a special mixing rule. Algorithm for local models’ parameters estimation and structure adjustment has been developed. Proposed approach allows straightforward designing of the combined feedforward/feedback co...

The present study emphasizes the forecast of Andhra Pradesh's total marine fish production and catch commercially important fishes, viz., Indian Mackerel, Oil Sardine, Horse Lesser Sardines for next 5 years by different statistical machine learning approaches under climate change scenario. Forecasting is done with without inclusion climatic environmental parameters in models. Exogenous variable...

The implementation of data mining has become very popular in many fields recently, including the petroleum industry. It is widely used to help decision-making processes order minimize oil losses during operations. One major causes loss flow blockages transport gathering facility, known as congeal phenomenon. To overcome this situation, real-time surveillance monitor condition inside pipes. Howe...

Beamspace multiple-input-multiple-output (MIMO) as a green technology can efficiently substitute for the conventional massive MIMO, provided that beamspace channel is acquired precisely. The prior efforts in this area of study, especially learning-driven ones, however, indicate remarkable performance losses owing to lack generalization. In paper, we propose modified non-linear auto-regressive e...

Non-linear dynamical systems are difficult to control due to the model uncertainties and external disturbances that may occur in these systems. This paper addresses the problem of identification using dynamic neural networks (DNNs) based on genetic algorithm (GA) for nonlinear dynamic systems. Four different dynamic neural networks are used for identification of the same nonlinear dynamic syste...

In this paper, a new correlation analysis based algorithm is proposed for the identification of a class of nonlinear systems which can be described by the NARX (Nonlinear AutoRegressive with eXogeneous input) model with input nonlinearities. Without any assumptions about the structure of an approximating function for the system nonlineariy, the algorithm recovers the functional values of the no...