IEEE Trans Neural Netw Learn Syst - A Neurodynamic Optimization Method for Recovery of Compressive Sensed Signals With Globally Converged Solution Approximating to l0 Minimization.

Tópicos

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Resumo

Finding the optimal solution to the constrained l0 -norm minimization problems in the recovery of compressive sensed signals is an NP-hard problem and it usually requires intractable combinatorial searching operations for getting the global optimal solution, unless using other objective functions (e.g., the l1 norm or lp norm) for approximate solutions or using greedy search methods for locally optimal solutions (e.g., the orthogonal matching pursuit type algorithms). In this paper, a neurodynamic optimization method is proposed to solve the l0 -norm minimization problems for obtaining the global optimum using a recurrent neural network (RNN) model. For the RNN model, a group of modified Gaussian functions are constructed and their sum is taken as the objective function for approximating the l0 norm and for optimization. The constructed objective function sets up a convexity condition under which the neurodynamic system is guaranteed to obtain the globally convergent optimal solution. An adaptive adjustment scheme is developed for improving the performance of the optimization algorithm further. Extensive experiments are conducted to test the proposed approach in this paper and the output results validate the effectiveness of the new method.

Resumo Limpo

find optim solut constrain l norm minim problem recoveri compress sens signal nphard problem usual requir intract combinatori search oper get global optim solut unless use object function eg l norm lp norm approxim solut use greedi search method local optim solut eg orthogon match pursuit type algorithm paper neurodynam optim method propos solv l norm minim problem obtain global optimum use recurr neural network rnn model rnn model group modifi gaussian function construct sum taken object function approxim l norm optim construct object function set convex condit neurodynam system guarante obtain global converg optim solut adapt adjust scheme develop improv perform optim algorithm extens experi conduct test propos approach paper output result valid effect new method

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