Neural Information Processing
25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 13–16, 2018, Proceedings, Part III
Book and Conference Proceedings
25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 13–16, 2018, Proceedings, Part III
Book and Conference Proceedings
25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 13–16, 2018, Proceedings, Part II
Book and Conference Proceedings
25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 13-16, 2018, Proceedings, Part IV
Book and Conference Proceedings
25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 13-16, 2018, Proceedings, Part I
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25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 13–16, 2018, Proceedings, Part VII
Book and Conference Proceedings
25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 13–16, 2018, Proceedings, Part VI
Book and Conference Proceedings
25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 13–16, 2018, Proceedings, Part V
Chapter and Conference Paper
In the originally published version of chapters 21 and 33 the name of the author **hua Sheng was incorrectly spelled as “**ghua Sheng.” It was corrected to “**hua Sheng.”
Chapter and Conference Paper
Incremental extreme learning machine (IELM), convex incremental extreme learning machine (C-IELM) and other variants of extreme learning machine (ELM) algorithms provide low computational complexity techniques...
Chapter and Conference Paper
This paper presents an approach based on the Lagrange programming neural network (LPNN) framework for target localization under the outlier situation. The problem is formulated as a minimization problem of the...
Chapter and Conference Paper
In the implementation of a neural network, some imperfect issues, such as precision error and thermal noise, always exist. They can be modeled as multiplicative noise. This paper studies the problem of trainin...
Article
In the conventional recursive least square (RLS) algorithm for multilayer feedforward neural networks, controlling the initial error covariance matrix can limit weight magnitude. However, the weight decay effe...
Article
Article
In the past three decades, the properties of associative networks has been extensively investigated. However, most existing results focus on the fault-free networks only. In implementation, network faults can ...