Fractals everywhere
Analog VLSI and neural systems
Analog VLSI and neural systems
Convergent activation dynamics in continuous time networks
Neural Networks
Efficient simulation of finite automata by neural nets
Journal of the ACM (JACM)
Graded State Machines: The Representation of Temporal Contingencies in Simple Recurrent Networks
Machine Learning - Connectionist approaches to language learning
The Induction of Dynamical Recognizers
Machine Learning - Connectionist approaches to language learning
Training second-order recurrent neural networks using hints
ML92 Proceedings of the ninth international workshop on Machine learning
Neural networks and the bias/variance dilemma
Neural Computation
Induction of finite-state languages using second-order recurrent networks
Neural Computation
Machine Learning - Special issue on multistrategy learning
Saturation at high gain in discrete time recurrent networks
Neural Networks
Learning finite machines with self-clustering recurrent networks
Neural Computation
Combining Symbolic and Neural Learning
Machine Learning
Fixed points in two-neuron discrete time recurrent networks: stability and bifurcation considerations
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Information Processing and VLSI
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Introduction To Automata Theory, Languages, And Computation
Introduction To Automata Theory, Languages, And Computation
Rule Revision With Recurrent Neural Networks
IEEE Transactions on Knowledge and Data Engineering
Computation: finite and infinite machines
Computation: finite and infinite machines
On the effect of analog noise in discrete-time analog computations
Neural Computation
Refining Numerical Constants in First Order Logic Theories
Machine Learning - Special issue on multistrategy learning
Approximating the Semantics of Logic Programs by Recurrent Neural Networks
Applied Intelligence
Rule Revision With Recurrent Neural Networks
IEEE Transactions on Knowledge and Data Engineering
Natural Language Grammatical Inference with Recurrent Neural Networks
IEEE Transactions on Knowledge and Data Engineering
Simple Strategies to Encode Tree Automata in Sigmoid Recursive Neural Networks
IEEE Transactions on Knowledge and Data Engineering
Generalization Ability of Folding Networks
IEEE Transactions on Knowledge and Data Engineering
Clustering and Classification in Structured Data Domains Using Fuzzy Lattice Neurocomputing (FLN)
IEEE Transactions on Knowledge and Data Engineering
Finite-state computation in analog neural networks: steps towards biologically plausible models?
Emergent neural computational architectures based on neuroscience
Inductive Bias in Recurrent Neural Networks
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
Robust Implementaion of Finite Automata by Recurrent RBF Networks
SOFSEM '00 Proceedings of the 27th Conference on Current Trends in Theory and Practice of Informatics
On the Need for a Neural Abstract Machine
Sequence Learning - Paradigms, Algorithms, and Applications
Finite-State Computation in Analog Neural Networks: Steps towards Biologically Plausible Models?
Emergent Neural Computational Architectures Based on Neuroscience - Towards Neuroscience-Inspired Computing
Sequence Learning - Paradigms, Algorithms, and Applications
Recursive self-organizing network models
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Rule Extraction from Recurrent Neural Networks: A Taxonomy and Review
Neural Computation
Spatiotemporal Connectionist Networks: A Taxonomy and Review
Neural Computation
Distributed recursive learning for shape recognition through multiscale trees
Image and Vision Computing
State-dependent computation using coupled recurrent networks
Neural Computation
Group-Linking Method: A Unified Benchmark for Machine Learning with Recurrent Neural Network
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Advances in Games Technology: Software, Models, and Intelligence
Simulation and Gaming
Real-time motion detection by lateral inhibition in accumulative computation
Engineering Applications of Artificial Intelligence
Algorithmic lateral inhibition formal model for real-time motion detection
EUROCAST'07 Proceedings of the 11th international conference on Computer aided systems theory
Object recognition by recursive learning of multiscale trees
WILF'03 Proceedings of the 5th international conference on Fuzzy Logic and Applications
Recurrent networks for structured data - A unifying approach and its properties
Cognitive Systems Research
Learning symbolic representations of hybrid dynamical systems
The Journal of Machine Learning Research
Adaptive finite state machine based visual autonomous navigation system
Engineering Applications of Artificial Intelligence
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Recurrent neural networks that are trained to behave like deterministic finite-state automata (DFAs) can show deteriorating performance when tested on long strings. This deteriorating performance can be attributed to the instability of the internal representation of the learned DFA states. The use of a sigmoidel discriminant function together with the recurrent structure contribute to this instability. We prove that a simple algorithm can construct second-order recurrent neural networks with a sparse interconnection topology and sigmoidal discriminant function such that the internal DFA state representations are stable, that is, the constructed network correctly classifies strings of arbitrary length. The algorithm is based on encoding strengths of weights directly into the neural network. We derive a relationship between the weight strength and the number of DFA states for robust string classification. For a DFA with n state and minput alphabet symbols, the constructive algorithm generates a “programmed” neural network with O(n) neurons and O(mn) weights. We compare our algorithm to other methods proposed in the literature.