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Abstract— By adding a negative self-feedback to the maximum neural network (MNN), we propose a new parallel algorithm for the broadcast scheduling problem in packet radio networks. The developed algorithm introduces richer and more flexible nonlinear dynamics, and can prevent the network from getting stuck at local minima. The algorithm is verified by(More)
We have noted that the local minima problem in the backpropagation algorithm is usually caused by update disharmony between weights connected to the hidden layer and the output layer. To solve this problem, we propose a modified error function with added terms. By adding one term to the conventional error function, the modified error function can harmonize(More)
In this paper, we proposed a fast method for improving the elastic net to solve the traveling salesman problem. A dynamic parameter strategy is introduced into the elastic net, which increases the ability of searching for the cities and helps the network get convergence with the optimal or near-optimal solution sooner. Simulations show that the proposed(More)
Hong & Lie (1993) defined joint failure importance(JFI) as a measure of how 2 components in a system interact in contributing to the system failure. Their definitions were given on the basis of Birnbaum importance, but there are some contradictions of the calculation for fault tree analysis. Hong’s importance measures mainly concern the 2(More)
Weixing Bi & Jianjun Chen (2009) proposed a new algorithm of Joint Failure Importance (JFI), The new JFI is a useful metrics to rank components regarding their impact on system performance. However their definitions regarding JFI were limited to statistically independent component states. This paper removes the statistical independence restriction by(More)
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