Titlebook: Advances in Neural Networks – ISNN 2018; 15th International S Tingwen Huang,Jiancheng Lv,Alexander V. Tuzikov Conference proceedings 2018 S

TIBIA

书目名称Advances in Neural Networks – ISNN 2018影响因子(影响力)




书目名称Advances in Neural Networks – ISNN 2018影响因子(影响力)学科排名




书目名称Advances in Neural Networks – ISNN 2018网络公开度




书目名称Advances in Neural Networks – ISNN 2018网络公开度学科排名




书目名称Advances in Neural Networks – ISNN 2018被引频次




书目名称Advances in Neural Networks – ISNN 2018被引频次学科排名




书目名称Advances in Neural Networks – ISNN 2018年度引用




书目名称Advances in Neural Networks – ISNN 2018年度引用学科排名




书目名称Advances in Neural Networks – ISNN 2018读者反馈




书目名称Advances in Neural Networks – ISNN 2018读者反馈学科排名

栖息地

Coordinating Plans of Autonomous Agentson neural network. The capsule network uses vector as input and output and dynamic routing updates parameters, which has better effect than convolution neural network. In this paper, a new activation function is proposed for the capsule network and the least weight loss is added to the loss function

不透明

粘土

Actions and plans in multiagent domains,chy of ensembles of Hopfield network representing definite classes of objects. Patterns in each of a network are considered as in some sense identical representatives of given class. These networks generate their self-reproducible descendants which can exchange patterns with each other and generate

enormous

Abduct

Ruptured-Disk

M. Ravikanth,T. K. Chandrashekarcal Artificial Neural Networks. Although there has been a wide range of research to improve the accuracy of SNNs, their performance is determined not only by accuracy, but also by speed and energy efficiency. In this study, we analyzed the relationship between hyperparameters, accuracy, speed and en

Herbivorous

M. Ravikanth,T. K. Chandrashekaraining data to reduce the design cost and enable applying cross-modal networks in sparse data environments. Two approaches for building X-CNNs are presented. The base approach learns the topology in a data-driven manner, by using measurements performed on the base CNN and supplied data. The iterativ

杀菌剂

Glower

C. L. Rollinson,E. W. Rosenbloomhitectures to perform better than shallow ones. This paper introduces complex-valued deep belief networks, which can be used for unsupervised pretraining of complex-valued deep neural networks. Experiments on the MNIST dataset using different network architectures show better results of the complex-