Abstract
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(1988), 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.
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Supported by Doctoral Special Fund of State Education Commission and the National Natural Science Foundation of China, Grant No.59477001 and No.59707002
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He, Y., Luo, X. & Qiu, G. A neural-based nonlinear L 1-norm optimization algorithm for diagnosis of networks. J. of Electron.(China) 15, 365–371 (1998). https://doi.org/10.1007/s11767-998-0011-1
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DOI: https://doi.org/10.1007/s11767-998-0011-1