Convergent activation dynamics in continuous time networks
Neural Networks
Introduction to the theory of neural computation
Introduction to the theory of neural computation
Subgrouping reduces complexity and speeds up learning in recurrent networks
Advances in neural information processing systems 2
Distributed Representations, Simple Recurrent Networks, And Grammatical Structure
Machine Learning - Connectionist approaches to language learning
The Induction of Dynamical Recognizers
Machine Learning - Connectionist approaches to language learning
Analysis of dynamical recognizers
Neural Computation
Handbook of formal languages, vol. 3
Dynamical recognizers: real time language recognition by analog computers
Theoretical Computer Science
On the effect of analog noise in discrete-time analog computations
Neural Computation
Co-Evolution in the Successful Learning of Backgammon Strategy
Machine Learning
The handbook of brain theory and neural networks
Recurrent networks: supervised learning
The handbook of brain theory and neural networks
Proceedings of the 1998 conference on Advances in neural information processing systems II
Neural networks and analog computation: beyond the Turing limit
Neural networks and analog computation: beyond the Turing limit
The theory of evolution strategies
The theory of evolution strategies
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
The Handbook of Brain Theory and Neural Networks
The Handbook of Brain Theory and Neural Networks
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Connectionist Approaches to Language Learning
Connectionist Approaches to Language Learning
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
Introduction To Automata Theory, Languages, And Computation
Introduction To Automata Theory, Languages, And Computation
Designing a Counter: Another Case Study of Dynamics and Activation Landscapes in Recurrent Networks
KI '97 Proceedings of the 21st Annual German Conference on Artificial Intelligence: Advances in Artificial Intelligence
Recurrent Neural Network Architectures: An Overview
Adaptive Processing of Sequences and Data Structures, International Summer School on Neural Networks, "E.R. Caianiello"-Tutorial Lectures
Using Prior Knowledge in a {NNPDA} to Learn Context-Free Languages
Advances in Neural Information Processing Systems 5, [NIPS Conference]
RAAM for Infinite Context-Free Languages
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 5 - Volume 5
Computation: finite and infinite machines
Computation: finite and infinite machines
A new evolutionary system for evolving artificial neural networks
IEEE Transactions on Neural Networks
Inductive inference from noisy examples using the hybrid finite state filter
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
LSTM recurrent networks learn simple context-free and context-sensitive languages
IEEE Transactions on Neural Networks
On learning context-free and context-sensitive languages
IEEE Transactions on Neural Networks
On the computational power of Elman-style recurrent networks
IEEE Transactions on Neural Networks
Gradient calculations for dynamic recurrent neural networks: a survey
IEEE Transactions on Neural Networks
Neural Information Processing
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In recent years it has been shown that first order recurrent neural networks trained by gradient-descent can learn not only regular but also simple context-free and context-sensitive languages. However, the success rate was generally low and severe instability issues were encountered. The present study examines the hypothesis that a combination of evolutionary hill climbing with incremental learning and a well-balanced training set enables first order recurrent networks to reliably learn context-free and mildly context-sensitive languages. In particular, we trained the networks to predict symbols in string sequences of the context-sensitive language {anbncn; n ≥ 1}. Comparative experiments with and without incremental learning indicated that incremental learning can accelerate and facilitate training. Furthermore, incrementally trained networks generally resulted in monotonic trajectories in hidden unit activation space, while the trajectories of non- incrementally trained networks were oscillating. The non-incrementally trained networks were more likely to generalise.