Because of massively parallel distributed nature and very fast
convergence rates, recurrent neural networks (RNN) are widely
applied to solving many problems in optimization, control and
robotic systems, etc. Hence, this book investigates the following
RNN models which solve some practical problems, together with their
corresponding analysis on stability and convergence. A type of
multilayer pole-assignment neural networks is applied to online
synthesizing and tuning feedback control systems. Then, a novel RNN
model is established by absorbing the first-order time-derivative
information to solve the Sylvester equation with time-varying
coefficient matrices. A dual neural network is developed to handle
quadratic programs subject to linear constraints. The Lagrangian
neural network and primal-dual neural network are also reviewed for
comparison purposes. The neural networks are then exploited for
real-time motion planning of redundant manipulators. The
publication is primarily intended for researchers and postgraduates
studying in RNN, control and robotics.
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