QA169 : A computational intelligent method for solving a class of min-max problems
Thesis > Central Library of Shahrood University > Mathematical Sciences > MSc > 2013
Authors:
Abstarct: In this thesis, we explain about norm, Jacobian matrix, Hessian matrix, Gradient
vector and convex function; next pay attention to optimality with the requirments
and show concepts of stability and energy function in dynamical system. then the
structure of the neural network model and mathematical model of neurons represent.
we want to state short history of neural network model to solve optimization problems
and previous models to solve optimization problems. The third chapter, we show one
model of neural network to solve convex nonlinear optimization problems baxsed on
two cases are likely, optimality, analysis of convex function, Lyapunov’s stability and
Lasalle’s invariance principal and stable equilibrium point of the neural network, we
proposed the optimal solution is a convex nonlinear programming problems. we also
show the proposed neural network is stable in the concept of Lyapunove and globally
convergent to an exact optimal solution to the original problem. several examples
are given to show the effectiveness of the proposed model. The last chapter, we will
describe an optimization technique that class of non-smooth optimization problems
are used. To demonstrate the efficiency of the model, some examples are solved using
this model.
Keywords:
#Neural network model #Stability #Lyapunov #Equilibrium point #Global convergance #Perceptron #Non-smoot Link
Keeping place: Central Library of Shahrood University
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Keeping place: Central Library of Shahrood University
Visitor: