In this paper, we address the state initialization problem in recurrent neural networks
(RNNs), which seeks proper values for the RNN initial states at the beginning of a prediction …

System Identification (SI) is a discipline in control engineering concerned with inferring
mathematical models from dynamic systems based on its input/output observations. Rich …

The Nonlinear Auto-Regressive Moving Average with Exogenous Inputs (NARMAX) model
is a powerful, efficient and unified representation of a variety of nonlinear models. The …

In this contribution we derive a computational Bayesian approach to NARMAX model
identification. The identification algorithm exploits continuing advances in computational …

Electroactive polymer actuators are important for soft robotics, but can be difficult to control
because of compliance, creep and nonlinearities. Because biological control mechanisms …

Accurate forecasting of airline passenger traffic is important for facilitating the effective
management and planning of aviation resources. In this study, we explore the air passenger …

A nonlinear structure will often respond periodically when it is excited with a sinusoidal
force. Several methods are available that can compute the periodic response for various …

This paper explores the capability of the Binary Artificial Bee Colony (BABC) algorithm for
feature selection of Nonlinear Autoregressive Moving Average with Exogenous Inputs …

Current models of dielectric elastomer actuators (DEAs) are mostly constrained to first
principal descriptions that are not well suited to the application of control design due to their …

A key challenge in synthetic biology is the development of effective methodologies for
characterization of component genetic parts in a form suitable for dynamic analysis and …