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原文連結
論文資訊
- 類型：已發表論文
- 日期：1998
摘要
We consider a model of analog computation which can recognize various 語言s in real time. We encode an input word as a point in R-d by composing iterated maps, and then apply inequalities to the resulting point to test for membership in the 語言. Each class of maps and inequalities, such as quadratic functions with rational coefficients, is capable of recognizing a particular class of 語言s. For instance, linear and quadratic maps can have both stack- like and queue-like memories. We use methods equivalent to the Vapnik-Chervonenkis dimension to separate some of our classes from each other: linear maps are less powerful than quadratic or piecewise-linear ones, polynomials are less powerful than elementary (trigonometric and exponential) maps, and deterministic polynomials of each degree are less
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