An advanced neural network topology and learning, applied for identification and control of a DC motor

2002 1st International IEEE Symposium

Volumn 1, Issue , 2002, Pages 289-295

  Baruch, I S ^a   Flores, J M ^a   Nara R, F ^a   Ramirez P I R ^a   Nenkova, B ^b  

^a DEPARTAMENTO DE FÍSICA   (Mexico)

^b BULGARIAN ACADEMY OF SCIENCES   (Bulgaria)

Author keywords

adaptive control; convergence; D.C. Motor; identification; Lyapunov function; Recurrent Neural Network; stability

Indexed keywords

BACKPROPAGATION; BACKPROPAGATION ALGORITHMS; CONVERGENCE OF NUMERICAL METHODS; DC MOTORS; ELECTRIC MACHINE CONTROL; IDENTIFICATION (CONTROL SYSTEMS); INTELLIGENT SYSTEMS; LEARNING ALGORITHMS; LYAPUNOV FUNCTIONS; NEURAL NETWORKS; RECURRENT NEURAL NETWORKS;

ADAPTIVE CONTROL; CONVERGENCE; DESIRED TRAJECTORIES; DIRECT ADAPTIVE CONTROL; DYNAMIC BACKPROPAGATION LEARNING ALGORITHMS; NEURAL NETWORK TOPOLOGY; RATE OF CONVERGENCE; RECURRENT TRAINABLE NEURAL NETWORKS;

ADAPTIVE CONTROL SYSTEMS;

EID: 84962795055     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IS.2002.1044270     Document Type: Conference Paper

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