Dynamic causal modeling (DCM) showed that a parallel multiple input model to striate and prestriate cortex accounts best for the MEG response data.
More recently, it has been shown (Pinter et al. 2008; Günther et al. 2009, 2011, 2012) that a multiple segment multiple-input model is required to model unconstrained quiet standing, and this clearly has implications for the corresponding human control system.
To illustrate the adaptive approach, multiple inputs and modeling techniques are utilized, including neural networks, particle swarm optimization, and denoising.
Good results are obtained with a non parametric MISO (Multiple Input Single Output) model (impulse response) provided by the multivariable correlation technique using real data from a commercial plant.
A multiple input, two output model is proposed which enables the two microphone acoustic intensity method to decompose the intensity vector into contributions from individual sources, even when they are coupled and in close proximity within the measurement surface.
The strategy considers a state space representation to model the pulp level of each bank (with a multiple input - single output model), which is obtained by a system identification procedure and uses a Kalman filter as a state estimator.
The Decoupled A-B Net (DABNet) nonlinear system identification model was used as the main D-RM type and was enhanced to model nonlinear multiple input and multiple output dynamic systems with options for double-pole formulation to handle fast/slow time scales and pole value optimization.
Both single input single output (SISO) and multiple input multiple output (MIMO) models are considered.
A multiple input, single output (MISO) model structure is given by the following set of rules: R i : if X is A i then y is B i ∀ i = 1, …, M (7).
Based on a nominal multiple input multiple output (MIMO) plant model and performance requirements, a pairing input-output analysis is performed to select the suitable input to control each output aiming to attenuate the loop coupling.
