The reported work aims to improve the performance of LSTM-based (Long Short-Term Memory) forecasting algorithms in cases NARX (Nonlinear Autoregressive with eXogenous input) models by using evolutionary search. proposed approach, ES-LSTM, combines a two-membered ES local search procedure (2MES) an ADAM optimizer train more accurate LSTMs. accuracy is measured from both error and trend predictio...

This paper presents multi-layer feedforward neural network-based identification and approximate predictive controller (NNAPC) for a two degree-of-freedom (DOF), quarter-car servohydraulic vehicle suspension system. The nonlinear dynamics of the servo-hydraulic actuator is incorporated in the suspension model. A suspension travel controller is developed to improve the ride comfort and handling q...

Abstract Nonlinear Auto-Regressive eXogenous input (NARX) models are a popular class of nonlinear dynamical models. Often polynomial basis expansion is used to describe the internal multivariate mapping (P-NARX). Resorting fixed functions convenient since it results in closed form solution estimation problem. The drawback, however, that predefined does not necessarily lead sparse representation...

System identification (SI) is the discipline of inferring mathematical models from unknown dynamic systems using input/output observations such with or without prior knowledge some system parameters. Many valid algorithms are available in literature, including Volterra series expansion, Hammerstein–Wiener models, nonlinear auto-regressive moving average model exogenous inputs (NARMAX) and its d...

Lithium-Ion batteries require step-ahead information to apply contingency plans prevent them from operating beyond their safe operation thresholds in grid storage and electric vehicle applications. Recently, machine learning techniques have been increasingly applied forecast one such battery metric, State-of-Charge % ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://w...

The management of irrigation main canals are studied in this research. One way improving is designing an efficient automatic control system the water that flows through canal pools, which usually carried out by PI controllers. However, since pools systems with large time delays and nonlinear hydrodynamics, these PIs tuned a very conservative so closed-loop instability may appear depending on ch...

Abstract: In order that the nth-order Generalized Frequency Response Function (GFRF) for nonlinear systems described by a NARX model can be directly written into a more straightforward and meaningful form in terms of the first order GFRF and model parameters, the nth-order GFRF is now determined by a new mapping function based on a parametric characteristic. This can explicitly unveil the linea...

Abstract Surrogate models play a vital role in overcoming the computational challenge designing and analyzing nonlinear dynamic systems, especially presence of uncertainty. This paper presents comparative study different surrogate modeling techniques for systems. Four methods, namely, Gaussian process (GP) regression, long short-term memory (LSTM) network, convolutional neural network (CNN) wit...