Inversion of feedforward neural networks: algorithms and applications
Inversion of feedforward neural networks: algorithms and applications
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前馈神经网络的反演：算法和应用
DOI：
10.1109/5.784232
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发表时间：
1999
期刊：
影响因子：
--
通讯作者：
C. Eggen
中科院分区：
文献类型：
--
作者：
C. A. Jensen;R. Reed;R. Marks;M. El;Jae;R. Miyamoto;G. Anderson;C. Eggen
有许多方法用于执行神经网络反演。多元演化反演程序能够同时找到多个反演点。约束神经网络反演要求反演解属于一个或多个指定的约束集。在许多情况下，神经网络反演解和约束集之间的迭代可以成功地解决约束反演问题。本文调查现有的神经网络反演方法，这是说明了它的使用作为一种工具，基于查询的学习，声纳性能分析，电力系统安全评估，控制和生成的码本向量。
There are many methods for performing neural network inversion. Multi-element evolutionary inversion procedures are capable of finding numerous inversion points simultaneously. Constrained neural network inversion requires that the inversion solution belong to one or more specified constraint sets. In many cases, iterating between the neural network inversion solution and the constraint set can successfully solve constrained inversion problems. This paper surveys existing methodologies for neural network inversion, which is illustrated by its use as a tool in query-based learning, sonar performance analysis, power system security assessment, control, and generation of codebook vectors.