The analysis of the frequency response integrated transmission-distribution networks with deep penetration solar photovoltaic (PV) generation faces major challenges due to complexity emerging from dynamic models numerous and diverse PV units involved. This article proposes converter-based equivalent for both distributed (distribution network-connected) large-scale (transmission units, which tak...

Dynamic system identification with Gaussian-process prior model is a probabilistic, nonparametric modelling method for identification. Gaussian-process models provide, besides the prediction, also the information about prediction uncertainty based on the availability or uncertainty of the data used for the modelling. An advantage of this kind of model is a small number of training parameters, a...

The derivation of a mathematical model from physical laws according to the use of the model (e.g., for control), is most basic here to determine the mathematical structure. For example, it is shown (Laszlo, 1972) that a close connection exists between dynamic identification of an environment and its control. System identification itself is a well developed area of system theory. The need of mat...

Design of controller for the process operation is typically based on the availability of the best model. Development of empirical model is incorporated with many assumptions and approximations. System identification is developed to alleviate this problem by considering the input and output data of the process for the model development. The cascaded tank process is highly nonlinear and non-minim...

in this study, a new approach for control (treatment) of AIDS based on patient entry drug. One of the main problems related to AIDS, is lack of control, lack of identification and early treatment of this disease. Today physicians more than anything, control the disease relying on their experience and knowledge and time-consuming and complex experiments, nevertheless, human errors are inevitable...

Potentially having a destructive influence on the mechanical properties of composite laminates, the invisible phenomenon of delamination frequently occurs under impact loading. In the present study, simulating the performance of long-gauge fiber Bragg grating sensors, impact-induced average strains within laminated composites are utilized to develop a delamination identification technique. Firs...

System identification is the process of creating models of dynamic process from inputoutput signals. The aim of system identification can be identified as “ to find a model with adjustable parameters and then to adjust them so that the predicted output matches the measured output”. This paper presents a method of modeling and simulating with system identification to achieve the maximum fitness ...

We investigate dynamic versions of fuzzy logic systems (FLS’s) and, specifically, their non-Singleton generalizations (NSFLS’s), and derive a dynamic learning algorithm to train the system parameters. The history-sensitive output of the dynamic systems gives them a significant advantage over static systems in modeling processes of unknown order. This is illustrated through an example in nonline...

Recurrent neural networks, especially for Elman Neural Network, have attracted the attention of researchers in the fields of Dynamic System Identification (DSI) since they took the memory unit through the context delay. In this paper, we propose an Adaptive Local Search (ALS) algorithm to train Elman Neural Network (ENN) for Dynamic Systems Identification (DSI) from a new angle instead of tradi...