模擬電路故障診斷L1估計(jì)及其神經(jīng)網(wǎng)絡(luò)解法
A NEURAL-BASED NONLINEAR L1 OPTIMIZATION ALGORITHM FOR DIAGNOSIS OF NETWORKS
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摘要: 基于精確罰函數(shù)法,提出了新的求解L1范致問題最優(yōu)解的神經(jīng)網(wǎng)絡(luò)方法,它避免了Kennedy和Chua(1988)網(wǎng)絡(luò)罰因子較大時(shí)性態(tài)變壞問題。對(duì)Bandler(1982)提出的模擬電路故障診斷L1范數(shù)法進(jìn)行了改進(jìn),將線性約束L1問題轉(zhuǎn)化為非線性約束L1問題,并用新的神經(jīng)網(wǎng)絡(luò)方法求解,計(jì)算量小。模擬實(shí)驗(yàn)表明,所提神經(jīng)網(wǎng)絡(luò)方法和改進(jìn)的模擬電路故障診斷L1范數(shù)方法是可行的。
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關(guān)鍵詞:
- 故障診斷; L1范數(shù); 神經(jīng)優(yōu)化
Abstract: Based on exact penalty function, a new neural-networks for solving the L1-norm optimization problem is proposed. In comparison with Kennedy and Chua s networks(1988), it has better properties. Based on Bandler s fault location method(1982), a new nonlinearly constrained L1 norm problem is developed. It can be solved with less computing time through only one optimization processing. The proposed neural networks can be used to solve the analog diagnosis L1 problem. The validity of the neural networks and the fault location L1 method are illustrated by extensive computer simulations. -
Bandler J W, et al. A linear programming approach to fault location in analog circuits. Proc. IEEE Int. Symp. CAS Chicago, IL: 1981, 256-261.[2]Bandler J W, et al. Fault isolation in linear analog circuits using the L1 norm. Proc. IEEE Int. Symp. CAS, Rome, Italy: 1982, 1140-1143.[3]Cichocki A, Unbehauen R. Neural networks for optimization and signal processing. John wiley, 1993, Chaps. 5, 7.[4]Pietrzykowski T. An exact potential method for constrained maxima. SIAM J. Num. Anal. 1969, 6(2): 299-304.[5]Kennedy M P, Chua L O. Neural networks for nonlinear programming. IEEE Trans. on CAS, 1988, 35(5): 554-562.[6]Charalambous C. On condition for optimality of the nonlinear L1 problem. SIAM J. Num. Anal. 1979,17(1): 123-135. -
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