Page 11 - Neural Network Modeling and Identification of Dynamical Systems

List of Acronyms

DS Deterministic system MIMO Multiple Input Multiple Output
VS Vague system MISO Multiple Input Single Output
CS Controllable system MPC Model predictive control
AS Adaptive system MRAC Model reference adaptive control
IS Intelligent system MSE Mean square error
SE Stereotyped environment NARMAX Nonlinear AutoRegressive network with Mov-
UE Uncertain environment ing Average and eXogeneous inputs
RE Reacting environment NARX Nonlinear AutoRegressive network with eXo-
AE Adaptive environment geneous inputs
IE Intelligent environment NASA National Aeronautical and Space Administra-
AC Adjusting controller tion
ANN Artificial neural network NASP National AeroSpace Plane
AWACS Airborne warning and control system NC Neural controller
BPTT Backpropagation through time NLP Nonlinear programming
CMA-ES Covariance Matrix Adaptive Evolution NM Network model
Strategy
ODE Ordinary differential equation
DAE Differential algebraic equation
DTDNN Distributed Time Delay Neural Network PDE Partial differential equation
EKF Extended Kalman filter PD, PI, PID Proportional-differential, proportional-integral,
ENC Ensemble of neural controllers proportional-integral-differential
FB Functional basis RBF Radial basis function
RLSM Recursive least-squares method
FTDNN Focused Time Delay Neural Network
GLONASS Global Navigation Satellite System RKNN Runge–Kutta Neural Network
GS Gain scheduling RM Reference model
GPS Global Positioning System RMLP Recurrent MultiLayer Perceptron
HRV Hypersonic research vehicle RMSE Root-mean-square error
IVP Initial value problem RTRL Real-Time Recurrent Learning
KM Kalman filter SISO Single Input Single Output
LDDN Layered Digital Dynamic Network TDL Time delay line, tapped delay line
MAC Mean aerodynamic chord TDNN Time Delay Neural Network
MLP MultiLayer Perceptron UAV Unmanned aerial vehicle
MDS Multimode dynamical system

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