Symbolic Processing in Neural Networks
Symbolic Processing in Neural Networks
复制标题
神经网络中的符号处理
DOI：
--
复制
发表时间：
2003
影响因子：
--
通讯作者：
José Félix Costa
中科院分区：
文献类型：
--
作者：
J. P. Neto;H. Siegelmann;José Félix Costa
在本文中，我们表明，编程语言可以被翻译成经常性的（模拟，理性加权）神经网络。在神经网络中实现编程语言不仅在理论上令人兴奋，而且在最近的符号和子符号计算合并的努力中也有一些实际意义。为了有一些用处，它应该在有限资源的上下文中进行。在这里，我们展示了如何使用资源边界来加速神经网络的计算，通过像通常的编程语言一样适当的数据类型编码。我们介绍了数据类型，并展示了如何编码并将其保持在神经网络的信息流中。数据类型和控制结构是称为NETDEF的适当编程语言的一部分。每个NETDEF程序都有一个特定的神经网络来计算它。这些网络具有强大的模块化结构和同步机制，尽管神经网络具有大量的并行功能，但它们允许顺序或并行执行。每个指令表示一个独立的神经网络。有用于赋值、条件和循环指令的构造函数。除了语言核心，许多其他功能也可以使用相同的方法。
In this paper we show that programming languages can be translated into recurrent (analog, rational weighted) neural nets. Implementation of programming languages in neural nets turns to be not only theoretical exciting, but has also some practical implications in the recent efforts to merge symbolic and sub symbolic computation. To be of some use, it should be carried in a context of bounded resources. Herein, we show how to use resource bounds to speed up computations over neural nets, through suitable data type coding like in the usual programming languages. We introduce data types and show how to code and keep them inside the information flow of neural nets. Data types and control structures are part of a suitable programming language called NETDEF. Each NETDEF program has a specific neural net that computes it. These nets have a strong modular structure and a synchronization mechanism allowing sequential or parallel execution of subnets, despite the massive parallel feature of neural nets. Each instruction denotes an independent neural net. There are constructors for assignment, conditional and loop instructions. Besides the language core, many other features are possible using the same method.