The phrase "adaptive back" is correct and usable in written English.
It can be used in contexts related to technology, design, or systems that adjust or respond to changes or user needs.
Example: "The new software features an adaptive back that adjusts the interface based on user preferences."
Alternatives: "responsive back" or "flexible back".
Adaptive back stepping control (ABC) is originally applied to a linearized model of an active magnetic bearing (AMB) system.
In this stage we investigate different learning algorithms of MLP neural networks that some of them, such as quick prop (QP) learning algorithm, extended delta-bar-delta (EDBD), super self adaptive back propagation (SuperSAB) and conjugate gradient (CG) are proposed for the first time in the area of communication signals recognition.
Feed-forward perceptrons are trained with three different back propagation algorithms, namely the adaptive back propagation algorithm of the steepest descent, the back propagation Levenberg Marquardt algorithm and the back propagation Bayesian algorithm.
For BP networks three different network training methods are employed, namely the adaptive back propagation algorithm of the steepest descent with the use of momentum term, the back propagation Levenberg Marquardt algorithm and the back propagation Bayesian algorithm.
More specifically, feed-forward artificial neural networks are trained with three different back propagation algorithms, namely the adaptive back propagation algorithm of the steepest descent with the use of momentum term, the back propagation Levenberg Marquardt algorithm and the back propagation Bayesian algorithm.
In this paper, an adaptive back-stepping control scheme was proposed for a helicopter with faults.
Adaptive back-stepping control is a recursive design method that employs the Lyapunov stability theory in its design procedures.
Adaptive back-stepping control is applied to the problem, which drives a continuous bioreactor to its optimal productivity point.
An improved fuzzy adaptive back-propagation algorithm is proposed and applied to the identification of mechanical parameters of the surrounding rock of underground caverns in stratified layers.
The excitation voltage control is obtained by means of adaptive back-stepping method and Lyapunov stability theory using a fourth-order two-input nonlinear model.
First, an excitation control input and a parameter updating law are obtained simultaneously via adaptive back-stepping and Lyapunov methods to achieve stability of the zero dynamics subsystem.
