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- C. Lee Giles, Clifford B. Miller, Dong Chen, Hsing-Hen Chen, Guo-Zheng Sun, Yee-Chun Lee
- Neural Computation
- 1992

Simple secood-order recurrent netwoIts are shown to readily learn sman brown regular grammars when trained with positive and negative strings examples. We show that similar methods are appropriate for learning unknown grammars from examples of their strings. TIle training algorithm is an incremental real-time, recurrent learning (RTRL) method that computes… (More)

- C. Lee Giles, Dong Chen, Guo-Zheng Sun, Hsing-Hen Chen, Yee-Chun Lee, Mark W. Goudreau
- IEEE Trans. Neural Networks
- 1995

It is often difficult to predict the optimal neural network size for a particular application. Constructive or destructive methods that add or subtract neurons, layers, connections, etc. might offer a solution to this problem. We prove that one method, recurrent cascade correlation, due to its topology, has fundamental limitations in representation and thus… (More)

- C. Lee Giles, Guo-Zheng Sun, Hsing-Hen Chen, Yee-Chun Lee, Dong Chen
- NIPS
- 1989

- Guo-Zheng Sun, Hsing-Hen Chen, Yee-Chun Lee
- NIPS
- 1991

The two well known learning algorithms of recurrent neural networks are the back-propagation (Rumelhart & el al., Werbos) and the forward propagation (Williams and Zipser). The main drawback of back-propagation is its off-line backward path in time for error cumulation. This violates the on-line requirement in many practical applications. Although the… (More)

- Guo-Zheng Sun, Hsing-Hen Chen, Yee-Chun Lee
- NIPS
- 1992

We proposed a model of Time Warping Invariant Neural Networks (TWINN) to handle the time warped continuous signals. Although TWINN is a simple modification of well known recurrent neural network, analysis has shown that TWINN completely removes time warping and is able to handle difficult classification problem. It is also shown that TWINN has certain… (More)

- Yee-Chun Lee, Hsing-Hen Chen, Guo-Zheng Sun
- Neural Networks
- 1988

- Guo-Zheng Sun, C. Lee Giles, Hsing-Hen Chen
- Summer School on Neural Networks
- 1997

Recurrent neural networks are dynamical network structures which have the capabilities of processing and generating temporal information. To our knowledge the earliest neural network model that processed temporal information was that of MeCulloch and Pitts [McCulloch43]. Kleene [Kleene56] extended this work to show the equivalence of finite automata and… (More)

- Guo-Zheng Sun, Yee-Chun Lee, Hsing-Hen Chen
- NIPS
- 1987

We propose a new scheme to construct neural networks to classify patterns. The new scheme has several novel features : 1. We focus attention on the important attributes of patterns in ranking order. Extract the most important ones first and the less important ones later. 2. In training we use the information as a measure instead of the error function. 3. A… (More)

Recently, Ott, Grebogi and Yorke (OGY) [6] found an effective method to control chaotic systems to unstable fixed points by using only small control forces; however, OGY's method is based on and limited to a linear theory and requires considerable knowledge of the dynamics of the system to be controlled. In this paper we use two radial basis function… (More)