模擬電路故障診斷L1估計及其神經(jīng)網(wǎng)絡解法
A NEURAL-BASED NONLINEAR L1 OPTIMIZATION ALGORITHM FOR DIAGNOSIS OF NETWORKS
-
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)絡方法,它避免了Kennedy和Chua(1988)網(wǎng)絡罰因子較大時性態(tài)變壞問題。對Bandler(1982)提出的模擬電路故障診斷L1范數(shù)法進行了改進,將線性約束L1問題轉化為非線性約束L1問題,并用新的神經(jīng)網(wǎng)絡方法求解,計算量小。模擬實驗表明,所提神經(jīng)網(wǎng)絡方法和改進的模擬電路故障診斷L1范數(shù)方法是可行的。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. -
計量
- 文章訪問數(shù): 2268
- HTML全文瀏覽量: 130
- PDF下載量: 371
- 被引次數(shù): 0
下載:
