A neural-based nonlinear L_1-Norm optimization algorithm for diagnosis of networks

摘要

Based on exact penalty function, a new neural network for solving the L_1-norm optimization problem is proposed. in comparison with Kennedy and Chua's network(1998), it has better properties. Based on Bandler's fault location method(1982), a new nonlinearly constrained L_1-norm problem is developed. It can be solved with less computing time through only one Optimization processing. The proposed neural network can be used to solve the analog diagnosis L_1 problem. The validity of the proposed neural networks and the fault location L_1 method are Illustrated by extensive computer simulations.