Particle swarm optimization algorithm (PSO) has been optimized from various aspects since
it was proposed. Optimization of PSO can be realized by optimizing its iterative process or …

Swarm Intelligence is applied to optimisation problems due to its robustness, scalability,
generality, and flexibility. Based on simple rules, simple reactive agents-swarm (eg fish, bird …

Increasing power demand and global warming are forcing the world towards the power
generation from renewable energy sources. But, wind power generation is highly uncertain …

This paper explores the application of Non-Linear Autoregressive Model with Exogeneous
Inputs (NARX) system identification of an essential oil extraction system. Model structure …

This paper presents an application of the Radial Basis Function Neural Network (RBFNN)-
based identification of an essential oil extraction using Non-Linear Autoregressive Model …

This paper explores the application of Non-Linear Autoregressive Model with Exogenous
Inputs (NARX) system identification of heat exchanger system. Model structure selection was …

This paper presents system identification-based approach to create a Non-linear Auto-
Regressive model with Exogenous (NARX)-based adaptive noise filter to remove noise from …

Due to continuous depleting of conventional energy reserves as well as global warming
issues has diverted world attention towards non conventional energy sources. Out of …

This paper explores the application of the Particle Swarm Optimization (PSO) algorithm for
parameter estimation of a Nonlinear Auto-Regressive with Exogeneous Model (NARX) of a …

In this paper, we present a Radial Basis Function Neural Network (RBFNN)-based
Nonlinear Auto-Regressive Model with Exegeneous Inputs (NARX) model of a DC motor …