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Learning Methods of Constructive Neural Network

机译：建设性神经网络学习方法

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Learning methods of constructive neural network aims to overcome the disadvantages of BP algorithm, which has many advantages such as fast convergent rate, less computation, good fault-tolerant and strong generalization ability, etc. Its structure is constructed step by step in the processing of the data rather than being prescribed in advance. This paper mainly introduces the developing motivations, research status quo and development directions, laying stress on two kinds of constructive learning methods: FP algorithm and covering algorithm. After discussing the construction and basic properties of FP network, we construct an FP network as a general clustering unit, and analyze its main properties. On the base of summarizing covering algorithm we present a general neighborhood covering algorithm and its corres-ponding network. According to the characteristics of the algorithm we analyze its existing problems and propose the further research orientations.
机译：建设性神经网络的学习方法旨在克服BP算法的缺点，这具有许多优点，如快速收敛速率，较少计算，良好的容错和强大的泛化能力等。其结构在处理过程中逐步构建数据而不是提前处方。本文主要介绍了开发的动机，研究现状和发展方向，奠定了两种建设性学习方法的压力：FP算法和覆盖算法。在讨论FP网络的构建和基本属性之后，我们将FP网络构建为一般集群单元，并分析其主要属性。关于总结覆盖算法的基础，我们介绍了一般邻域覆盖算法及其腐蚀网络。根据算法的特征，我们分析了其现有问题并提出了进一步的研究方向。

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