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- W2364879461 abstract "The paper constructs a new optimal target function for feed forward neural networks according to convex optimization theory and constraint optimization theory. One of the important characteristics of the target function is as follows: if the connecting weight values are fixed, it is convex in the outputs of hidden layers; if the outputs of hidden layers are fixed, it is convex in the connecting weight values. When the target function is optimized, the outputs of hidden layers are treated as the optimized variables, the connecting weight values and the outputs of hidden layers are optimized alternately; then the punishment factor is increased and the previous optimal method is repeated until the punishment factor is big enough. The convex optimization algorithm for feed forward neural networks by the new optimal target function overcomes to the extent the disadvantage in the previous algorithms that networks training enters local minimum easily and fails. The new algorithm is analyzed in theory and practice. The paper analyzes the important role of the punishment factor in the new algorithm, and validates it by image compress experiment." @default.
- W2364879461 created "2016-06-24" @default.
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- W2364879461 date "2004-01-01" @default.
- W2364879461 modified "2023-09-25" @default.
- W2364879461 title "Self-adaptation Analysis and Application of Convex Optimization Algorithm for Feed forward Neural Networks" @default.
- W2364879461 hasPublicationYear "2004" @default.
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