A Nonfeasible Quadratic Approximation Recurrent Neural Network for Equality Constrained Optimization Problems
نویسندگان
چکیده
Convex optimization techniques are widely used in the design and analysis of communication systems and signal processing algorithms. In this paper a novel recurrent neural network is presented for solving nonlinear strongly convex equality constrained optimization problems. The proposed neural network is based on recursive quadratic programming for nonlinear optimization, in conjunction with homotopy method for solving nonlinear algebraic equations. It constructs generally a non-feasible trajectory which satisfies the constraints as . The boundedness of solutions and the global convergence to the optimal point of the problem are proven. The correctness and the performance of the proposed neural network are evaluated by simulation results on illustrative numerical examples.
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