376 research outputs found
An EMO Joint Pruning with Multiple Sub-networks: Fast and Effect
The network pruning algorithm based on evolutionary multi-objective (EMO) can
balance the pruning rate and performance of the network. However, its
population-based nature often suffers from the complex pruning optimization
space and the highly resource-consuming pruning structure verification process,
which limits its application. To this end, this paper proposes an EMO joint
pruning with multiple sub-networks (EMO-PMS) to reduce space complexity and
resource consumption. First, a divide-and-conquer EMO network pruning framework
is proposed, which decomposes the complex EMO pruning task on the whole network
into easier sub-tasks on multiple sub-networks. On the one hand, this
decomposition reduces the pruning optimization space and decreases the
optimization difficulty; on the other hand, the smaller network structure
converges faster, so the computational resource consumption of the proposed
algorithm is lower. Secondly, a sub-network training method based on
cross-network constraints is designed so that the sub-network can process the
features generated by the previous one through feature constraints. This method
allows sub-networks optimized independently to collaborate better and improves
the overall performance of the pruned network. Finally, a multiple sub-networks
joint pruning method based on EMO is proposed. For one thing, it can accurately
measure the feature processing capability of the sub-networks with the
pre-trained feature selector. For another, it can combine multi-objective
pruning results on multiple sub-networks through global performance impairment
ranking to design a joint pruning scheme. The proposed algorithm is validated
on three datasets with different challenging. Compared with fifteen advanced
pruning algorithms, the experiment results exhibit the effectiveness and
efficiency of the proposed algorithm
A Normalized Fuzzy Neural Network and its Application
A normal fuzzy neural network(NFNN) with five layers is proposed. Focusing on the structure optimization of network, a new node selection method and corresponding back propagation learning algorithm rules are presented In the case with fewer input nodes, the training is more fast in this kind of neural network. Water-flooded zone identification in measure-well explanation is an important problem in the oil field development; especially in its later period. Complex geology conditions lead to many fuzzy characters in measure-well curves. In the combination of all kinds of fuzzy conditions, oil water-flooded behaves as strong water-flooded, middle water-flooded, weak water-flooded and no water-flooded, etc. NFNN is applied to water-flooded identification in oil well measure-well to find its mapping relation between well measure-well and water-flooded level,accordingly realize the water-flooded zone identification in measure-well explanation of fuzzy oil. Test results illustrate its practicabilit
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