"model" .
.
_:N5cfafc23b06f4ec4a9c1fcd47a8f4d71 "pub.1084933419" .
_:Nb3cdf29c234e44e5a34d65c27a8be7bc .
"system" .
"behavior" .
.
.
.
.
.
.
"neural control strategy" .
"first step" .
.
"discrete-time nonlinear systems" .
_:Nd5a9b6038a014f38b3ac873d48f0ff47 .
.
"Imen" .
.
.
"uncertainty" .
"training algorithm" .
_:N66675660742a45c99fd4127a28f4eac3 _:N84a36407b8bc4189bd45b18d6c5508ec .
"neural model" .
"class" .
"simulation example" .
"best configuration" .
.
"backpropagation training algorithm" .
"2017-04-19" .
"robust neural control" .
.
"algorithm" .
"Mathematical Sciences" .
"article" .
.
"effectiveness" .
.
"2022-10-01T06:42" .
.
"nonlinear systems" .
"control scheme" .
"work" .
"configuration" .
.
"controller" .
_:N7717154d5ca34893b677451e6c4b93f3 .
_:Na1b02c24a055479cb4ebf062c40d77c1 .
"Robust Neural Control of Discrete Time Uncertain Nonlinear Systems Using Sliding Mode Backpropagation Training Algorithm" .
"learning techniques" .
"Mohamed" .
_:Nec6ce899139e4938a6478059c1a7487b .
"control" .
.
.
"Zaidi" .
"robustness" .
"Machine Intelligence Research" .
"step" .
"uncertain systems" .
_:N84a36407b8bc4189bd45b18d6c5508ec .
"backpropagation algorithm" .
"discrete-time uncertain nonlinear systems" .
.
"articles" .
"scheme" .
"classical BP" .
"Djemel" .
.
_:Nd5a9b6038a014f38b3ac873d48f0ff47 .
"example" .
"technique" .
"Springer Nature" .
_:Nec6ce899139e4938a6478059c1a7487b .
_:N5cfafc23b06f4ec4a9c1fcd47a8f4d71 "dimensions_id" .
.
"BP" .
.
"neural control" .
.
"Control & Energy Management Laboratory, National School of Sfax Engineers, University of Sfax, 3038, Sfax, Tunisia" .
"Control & Energy Management Laboratory, National School of Sfax Engineers, University of Sfax, 3038, Sfax, Tunisia" .
.
_:Na1b02c24a055479cb4ebf062c40d77c1 .
"important properties" .
.
.
.
"properties" .
"Chtourou" .
"Mohamed" .
_:N5cfafc23b06f4ec4a9c1fcd47a8f4d71 .
_:Nec6ce899139e4938a6478059c1a7487b _:N66675660742a45c99fd4127a28f4eac3 .
"2731-5398" .
"parametric uncertainties" .
"https://scigraph.springernature.com/explorer/license/" .
.
"speedy learning" .
_:Nb3cdf29c234e44e5a34d65c27a8be7bc "2" .
_:N7717154d5ca34893b677451e6c4b93f3 .
"control strategy" .
_:N84a36407b8bc4189bd45b18d6c5508ec .
"This work deals with robust inverse neural control strategy for a class of single-input single-output (SISO) discrete-time nonlinear system affected by parametric uncertainties. According to the control scheme, in the first step, a direct neural model (DNM) is used to learn the behavior of the system, then, an inverse neural model (INM) is synthesized using a specialized learning technique and cascaded to the uncertain system as a controller. In previous works, the neural models are trained classically by backpropagation (BP) algorithm. In this work, the sliding mode-backpropagation (SM-BP) algorithm, presenting some important properties such as robustness and speedy learning, is investigated. Moreover, four combinations using classical BP and SM-BP are tested to determine the best configuration for the robust control of uncertain nonlinear systems. Two simulation examples are treated to illustrate the effectiveness of the proposed control strategy." .
"https://doi.org/10.1007/s11633-017-1062-2" .
.
.
.
.
.
_:Nd5a9b6038a014f38b3ac873d48f0ff47 "doi" .
"learning" .
"inverse neural models" .
_:N7717154d5ca34893b677451e6c4b93f3 "16" .