On the public citation graph

Representation of Finite State Automata in Recurrent Radial Basis Function Networks

https://doi.org/10.1007/bf00116897
0 signed · 134 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/3-540-57292-9_45openalex-snapshot
Projecting sub-symbolic onto symbolic representations in artificial neural networks
inferredcites →https://doi.org/10.1007/978-94-009-0529-0openalex-snapshot
Neural and Automata Networks: Dynamical Behavior and Applications
inferredcites →https://doi.org/10.1007/bf00114843openalex-snapshot
Graded state machines: The representation of temporal contingencies in simple recurrent networks
inferredcites →https://doi.org/10.1007/bf00114844openalex-snapshot
Distributed representations, simple recurrent networks, and grammatical structure
inferredcites →https://doi.org/10.1007/bf00114845openalex-snapshot
The induction of dynamical recognizers
inferredcites →https://doi.org/10.1007/bf00993103openalex-snapshot
Extracting Refined Rules from Knowledge-Based Neural Networks
inferredcites →https://doi.org/10.1007/bf00993982openalex-snapshot
Combining Symbolic and Neural Learning
inferredcites →https://doi.org/10.1007/bf02478259openalex-snapshot
A logical calculus of the ideas immanent in nervous activity
inferredcites →https://doi.org/10.1016/0167-6393(90)90044-aopenalex-snapshot
Connected recognition with a recurrent network
inferredcites →https://doi.org/10.1016/0167-8655(92)90035-xopenalex-snapshot
Learning the dynamic nature of speech with back-propagation for sequences
inferredcites →https://doi.org/10.1016/0378-4754(81)90068-9openalex-snapshot
Introduction to automata theory, languages and computation
inferredcites →https://doi.org/10.1016/0885-064x(90)90006-yopenalex-snapshot
Learning from hints in neural networks
inferredcites →https://doi.org/10.1016/0950-7051(96)81916-2openalex-snapshot
Recurrent neural networks and prior knowledge for sequence processing: a constrained nondeterministic approach
inferredcites →https://doi.org/10.1016/b978-1-55860-247-2.50051-6openalex-snapshot
Training Second-Order Recurrent Neural Networks using Hints
inferredcites →https://doi.org/10.1016/s0019-9958(67)91165-5openalex-snapshot
Language identification in the limit
inferredcites →https://doi.org/10.1023/a:1022647012398openalex-snapshot
Graded State Machines: The Representation of Temporal Contingencies in Simple Recurrent Networks
inferredcites →https://doi.org/10.1023/a:1022651113306openalex-snapshot
The Induction of Dynamical Recognizers
inferredcites →https://doi.org/10.1023/a:1022699029236openalex-snapshot
Distributed Representations, Simple Recurrent Networks, And Grammatical Structure
inferredcites →https://doi.org/10.1109/29.21701openalex-snapshot
Phoneme recognition using time-delay neural networks
inferredcites →https://doi.org/10.1109/34.107014openalex-snapshot
On the problem of local minima in backpropagation
inferredcites →https://doi.org/10.1109/69.382304openalex-snapshot
Unified integration of explicit knowledge and learning by example in recurrent networks
inferredcites →https://doi.org/10.1109/72.125865openalex-snapshot
Translation, rotation, and scale invariant pattern recognition by high-order neural networks and moment classifiers
inferredcites →https://doi.org/10.1109/72.165604openalex-snapshot
Can backpropagation error surface not have local minima
inferredcites →https://doi.org/10.1109/72.279181openalex-snapshot
Learning long-term dependencies with gradient descent is difficult
inferredcites →https://doi.org/10.1109/72.279182openalex-snapshot
On the problem of local minima in recurrent neural networks
inferredcites →https://doi.org/10.1109/icnn.1994.374417openalex-snapshot
Constructing deterministic finite-state automata in sparse recurrent neural networks
inferredcites →https://doi.org/10.1109/ijcnn.1989.118644openalex-snapshot
Backpropagation separates when perceptrons do
inferredcites →https://doi.org/10.1109/ijcnn.1991.155283openalex-snapshot
An unified approach for integrating explicit knowledge and learning by example in recurrent networks
inferredcites →https://doi.org/10.1109/pgec.1965.264137openalex-snapshot
Geometrical and Statistical Properties of Systems of Linear Inequalities with Applications in Pattern Recognition
inferredcites →https://doi.org/10.1117/12.152638openalex-snapshot
Multilayered networks and the C-G uncertainty principle
inferredcites →https://doi.org/10.1142/s0218001493000431openalex-snapshot
EXPERIMENTAL COMPARISON OF THE EFFECT OF ORDER IN RECURRENT NEURAL NETWORKS
inferredcites →https://doi.org/10.1145/356914.356918openalex-snapshot
Inductive Inference: Theory and Methods
inferredcites →https://doi.org/10.1162/neco.1989.1.1.151openalex-snapshot
What Size Net Gives Valid Generalization?
inferredcites →https://doi.org/10.1162/neco.1989.1.2.270openalex-snapshot
A Learning Algorithm for Continually Running Fully Recurrent Neural Networks
inferredcites →https://doi.org/10.1162/neco.1989.1.2.281openalex-snapshot
Fast Learning in Networks of Locally-Tuned Processing Units
inferredcites →https://doi.org/10.1162/neco.1989.1.3.372openalex-snapshot
Finite State Automata and Simple Recurrent Networks
inferredcites →https://doi.org/10.1162/neco.1990.2.4.490openalex-snapshot
An Efficient Gradient-Based Algorithm for On-Line Training of Recurrent Network Trajectories
inferredcites →https://doi.org/10.1162/neco.1991.3.3.418openalex-snapshot
Including Hints in Training Neural Nets
inferredcites →https://doi.org/10.1162/neco.1992.4.1.1openalex-snapshot
Neural Networks and the Bias/Variance Dilemma
inferredcites →https://doi.org/10.1162/neco.1992.4.3.393openalex-snapshot
Learning and Extracting Finite State Automata with Second-Order Recurrent Neural Networks
inferredcites →https://doi.org/10.1162/neco.1992.4.3.406openalex-snapshot
Induction of Finite-State Languages Using Second-Order Recurrent Networks
inferredcites →https://doi.org/10.1162/neco.1993.5.6.976openalex-snapshot
Learning Finite State Machines With Self-Clustering Recurrent Networks
inferredcites →https://doi.org/10.1207/s15516709cog1402_1openalex-snapshot
Finding Structure in Time
inferredcites →https://doi.org/10.1364/ao.26.004972openalex-snapshot
Learning, invariance, and generalization in high-order neural networks
inferredcites →https://doi.org/10.21236/ada120123openalex-snapshot
Learning of Construction of Finite Automata from Examples Using Hill- Climbing. RR: Regular Set Recognizer
inferredcites →https://doi.org/10.21236/ada164453openalex-snapshot
Learning Internal Representations by Error Propagation

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/047134608x.w5113openalex-snapshot
Neural Nets, Recurrent
inferred← citeshttps://doi.org/10.1007/10719871_18openalex-snapshot
Understanding State Space Organization in Recurrent Neural Networks with Iterative Function Systems Dynamics
inferred← citeshttps://doi.org/10.1007/10719871_9openalex-snapshot
Fuzzy Knowledge and Recurrent Neural Networks: A Dynamical Systems Perspective
inferred← citeshttps://doi.org/10.1007/11539117_95openalex-snapshot
Learning Beyond Finite Memory in Recurrent Networks of Spiking Neurons
inferred← citeshttps://doi.org/10.1007/3-540-44411-4_32openalex-snapshot
Robust Implementation of Finite Automata by Recurrent RBF Networks
inferred← citeshttps://doi.org/10.1007/3-540-44565-x_5openalex-snapshot
Bidirectional Dynamics for Protein Secondary Structure Prediction
inferred← citeshttps://doi.org/10.1007/3-540-44597-8_34openalex-snapshot
Finite-State Computation in Analog Neural Networks: Steps towards Biologically Plausible Models?
inferred← citeshttps://doi.org/10.1007/3-540-48050-1_41openalex-snapshot
Recognizing and Discovering Complex Events in Sequences
inferred← citeshttps://doi.org/10.1007/3-540-63576-9_92openalex-snapshot
Refining numerical terms in horn clauses
inferred← citeshttps://doi.org/10.1007/3-540-63576-9_94openalex-snapshot
Inductive inference of tree automata by recursive neural networks
inferred← citeshttps://doi.org/10.1007/978-1-4471-5571-3_11openalex-snapshot
Recurrent Neural Networks
inferred← citeshttps://doi.org/10.1007/978-1-4471-7452-3_12openalex-snapshot
Recurrent Neural Networks
inferred← citeshttps://doi.org/10.1007/978-3-030-30487-4_25openalex-snapshot
On the Interpretation of Recurrent Neural Networks as Finite State Machines
inferred← citeshttps://doi.org/10.1007/978-3-642-32909-8_31openalex-snapshot
3D Vision-Based Autonomous Navigation System Using ANN and Kinect Sensor
inferred← citeshttps://doi.org/10.1007/bfb0054002openalex-snapshot
Recursive neural networks and automata
inferred← citeshttps://doi.org/10.1007/bfb0054003openalex-snapshot
The neural network pushdown automaton: Architecture, dynamics and training
inferred← citeshttps://doi.org/10.1016/0893-6080(95)00086-0openalex-snapshot
Extraction of rules from discrete-time recurrent neural networks
inferred← citeshttps://doi.org/10.1016/0950-7051(96)81916-2openalex-snapshot
Recurrent neural networks and prior knowledge for sequence processing: a constrained nondeterministic approach
inferred← citeshttps://doi.org/10.1016/b978-0-08-100659-7.00005-1openalex-snapshot
Deep Architectures
inferred← citeshttps://doi.org/10.1016/b978-0-08-100659-7.00006-3openalex-snapshot
Learning and Reasoning With Constraints
inferred← citeshttps://doi.org/10.1016/b978-0-08-100659-7.00020-8openalex-snapshot
Bibliography
inferred← citeshttps://doi.org/10.1016/j.biosystems.2006.04.027openalex-snapshot
Modelling of syntactical processing in the cortex
inferred← citeshttps://doi.org/10.1016/j.engappai.2013.12.006openalex-snapshot
Adaptive finite state machine based visual autonomous navigation system
inferred← citeshttps://doi.org/10.1016/j.fss.2008.05.005openalex-snapshot
Extracting symbolic knowledge from recurrent neural networks—A fuzzy logic approach
inferred← citeshttps://doi.org/10.1016/j.imavis.2006.01.022openalex-snapshot
Distributed recursive learning for shape recognition through multiscale trees
inferred← citeshttps://doi.org/10.1016/j.neucom.2003.12.005openalex-snapshot
Robust RBF finite automata
inferred← citeshttps://doi.org/10.1016/j.neucom.2020.03.014openalex-snapshot
A2: Extracting cyclic switchings from DOB-nets for rejecting excessive disturbances
inferred← citeshttps://doi.org/10.1016/s0165-0114(00)00069-5openalex-snapshot
Identification of fuzzy dynamic systems using Max–Min recurrent neural networks
inferred← citeshttps://doi.org/10.1016/s0893-6080(00)00037-xopenalex-snapshot
Stable behavior in a recurrent neural network for a finite state machine
inferred← citeshttps://doi.org/10.1016/s0893-6080(02)00221-6openalex-snapshot
On-line identification and reconstruction of finite automata with generalized recurrent neural networks
inferred← citeshttps://doi.org/10.1016/s0925-2312(00)00312-xopenalex-snapshot
Competitive radial basis functions training for phone classification
inferred← citeshttps://doi.org/10.1016/s0925-2312(97)00011-8openalex-snapshot
Recurrent neural networks: A constructive algorithm, and its properties
inferred← citeshttps://doi.org/10.1017/s0140525x97001581openalex-snapshot
The neural basis of cognitive development: A constructivist manifesto
inferred← citeshttps://doi.org/10.1023/a:1007686007399openalex-snapshot
Refining Numerical Constants in First Order Logic Theories
inferred← citeshttps://doi.org/10.1023/a:1008376514077openalex-snapshot
Approximating the Semantics of Logic Programs by Recurrent Neural Networks
inferred← citeshttps://doi.org/10.1081/e-elis4-120008648openalex-snapshot
Artificial Neural Networks and Natural Language Processing
inferred← citeshttps://doi.org/10.1088/2634-4386/adc32dopenalex-snapshot
General and stable emulation of finite state machines with spiking neural networks
inferred← citeshttps://doi.org/10.1093/ietfec/e90-a.12.2916openalex-snapshot
Group-Linking Method: A Unified Benchmark for Machine Learning with Recurrent Neural Network
inferred← citeshttps://doi.org/10.1109/5.784244openalex-snapshot
Equivalence in knowledge representation: automata, recurrent neural networks, and dynamical fuzzy systems
inferred← citeshttps://doi.org/10.1109/72.508949openalex-snapshot
Comments on "Constructive learning of recurrent neural networks: limitations of recurrent cascade correlation and a simple solution"
inferred← citeshttps://doi.org/10.1109/72.536317openalex-snapshot
Input-output HMMs for sequence processing
inferred← citeshttps://doi.org/10.1109/72.548181openalex-snapshot
Computational capabilities of local-feedback recurrent networks acting as finite-state machines
inferred← citeshttps://doi.org/10.1109/72.668898openalex-snapshot
Inductive inference from noisy examples using the hybrid finite state filter
inferred← citeshttps://doi.org/10.1109/72.712151openalex-snapshot
A general framework for adaptive processing of data structures
inferred← citeshttps://doi.org/10.1109/72.750555openalex-snapshot
Extracting finite-state representations from recurrent neural networks trained on chaotic symbolic sequences
inferred← citeshttps://doi.org/10.1109/91.660809openalex-snapshot
Fuzzy finite-state automata can be deterministically encoded into recurrent neural networks
inferred← citeshttps://doi.org/10.1109/ccece.2004.1347583openalex-snapshot
Representing generalized fuzzy automata in recurrent neural networks
inferred← citeshttps://doi.org/10.1109/cine48825.2020.234394openalex-snapshot
Learning Interpretable Hidden State Structures for Handwritten Numeral Recognition
inferred← citeshttps://doi.org/10.1109/codit49905.2020.9263967openalex-snapshot
Extracting finite state representations from recurrent models of Industrial Cyber Physical Systems
inferred← citeshttps://doi.org/10.1109/icetet.2008.15openalex-snapshot
A Connectionist Approach to Learn Marathi Language
inferred← citeshttps://doi.org/10.1109/icmla.2011.156openalex-snapshot
Learning to Tag from Logic Constraints in Hyperlinked Environments
inferred← citeshttps://doi.org/10.1109/icnn.1996.549038openalex-snapshot
Representation of fuzzy finite state automata in continuous recurrent, neural networks
inferred← citeshttps://doi.org/10.1109/iconip.2002.1198217openalex-snapshot
A new recurrent radial basis function network
inferred← citeshttps://doi.org/10.1109/ijcnn.2005.1555854openalex-snapshot
A recurrent RBF network model for nearest neighbor classification
inferred← citeshttps://doi.org/10.1109/ijcnn.2009.5178667openalex-snapshot
Semi-supervised clustering using similarity neural networks
inferred← citeshttps://doi.org/10.1109/ijcnn.2011.6033339openalex-snapshot
A tool to implement probabilistic automata in RAM-based neural networks
inferred← citeshttps://doi.org/10.1109/ijcnn.2015.7280838openalex-snapshot
Brains as naturally emerging turing machines
inferred← citeshttps://doi.org/10.1109/ijcnn.2016.7727420openalex-snapshot
Brains as optimal emergent Turing Machines
inferred← citeshttps://doi.org/10.1109/ijcnn.2017.7966445openalex-snapshot
Emulation of finite state automata with networks of synfire rings
inferred← citeshttps://doi.org/10.1109/ijcnn.2018.8489700openalex-snapshot
Automata Computation with Hodgkin-Huxley Based Neural Networks Composed of Synfire Rings
inferred← citeshttps://doi.org/10.1109/sbr-lars.2012.22openalex-snapshot
Vision-Based Autonomous Topological Navigation in Outdoor Environments
inferred← citeshttps://doi.org/10.1109/tamd.2013.2258398openalex-snapshot
Brain-Like Emergent Temporal Processing: Emergent Open States
inferred← citeshttps://doi.org/10.1109/tnn.2003.820839openalex-snapshot
Markovian Architectural Bias of Recurrent Neural Networks
inferred← citeshttps://doi.org/10.1109/tnn.2004.837585openalex-snapshot
Encoding Nondeterministic Fuzzy Tree Automata Into Recursive Neural Networks
inferred← citeshttps://doi.org/10.1109/tnn.2008.2000393openalex-snapshot
A New Approach to Knowledge-Based Design of Recurrent Neural Networks
inferred← citeshttps://doi.org/10.1109/tnn.2010.2059040openalex-snapshot
Identification of Finite State Automata With a Class of Recurrent Neural Networks
inferred← citeshttps://doi.org/10.1142/s0219622012500198openalex-snapshot
A NEW RBF NEURAL NETWORK FOR PREDICTION IN INDUSTRIAL CONTROL
inferred← citeshttps://doi.org/10.1142/s0219843620500164openalex-snapshot
Autonomous Programming for General Purposes: Theory
inferred← citeshttps://doi.org/10.1145/235809.235811openalex-snapshot
Constructing deterministic finite-state automata in recurrent neural networks
inferred← citeshttps://doi.org/10.11606/d.55.2012.tde-06122012-143729openalex-snapshot
NeuroFSM: aprendizado de Autômatos Finitos através do uso de Redes Neurais Artificiais aplicadas à robôs móveis e veículos autônomos
inferred← citeshttps://doi.org/10.1162/089976600300015097openalex-snapshot
Stable Encoding of Finite-State Machines in Discrete-Time Recurrent Neural Nets with Sigmoid Units
inferred← citeshttps://doi.org/10.1162/08997660360675099openalex-snapshot
Architectural Bias in Recurrent Neural Networks: Fractal Analysis
inferred← citeshttps://doi.org/10.1162/0899766053630350openalex-snapshot
Rule Extraction from Recurrent Neural Networks: ATaxonomy and Review
inferred← citeshttps://doi.org/10.1162/089976606775623360openalex-snapshot
Learning Beyond Finite Memory in Recurrent Networks of Spiking Neurons
inferred← citeshttps://doi.org/10.1162/089976698300017359openalex-snapshot
On the Effect of Analog Noise in Discrete-Time Analog Computations
inferred← citeshttps://doi.org/10.1162/neco.1996.8.4.675openalex-snapshot
Stable Encoding of Large Finite-State Automata in Recurrent Neural Networks with Sigmoid Discriminants
inferred← citeshttps://doi.org/10.1162/neco.2006.18.3.591openalex-snapshot
Learning Beyond Finite Memory in Recurrent Networks of Spiking Neurons
inferred← citeshttps://doi.org/10.1162/neco_a_01111openalex-snapshot
An Empirical Evaluation of Rule Extraction from Recurrent Neural Networks
inferred← citeshttps://doi.org/10.18725/oparu-11openalex-snapshot
Adaptive processing of structural data: from sequences to trees and beyond
inferred← citeshttps://doi.org/10.3166/ria.19.913-950openalex-snapshot
Utilisation des réseaux de neurones temporels pour le pronostic et la surveillance dynamique. Etude comparative de trois réseaux de neurones récurrents
inferred← citeshttps://doi.org/10.4236/ijis.2015.52011openalex-snapshot
Brain as an Emergent Finite Automaton: A Theory and Three Theorems
inferred← citeshttps://doi.org/10.4271/02-13-02-0007openalex-snapshot
Automated Diagnosis of Engine Misfire Faults Using Combination Classifiers
inferred← citeshttps://doi.org/10.5120/ijca2019918766openalex-snapshot
An Improved Neural Approaches for Forecasting Demand in Supply Chain Management
inferred← citeshttps://doi.org/10.5402/2012/324194openalex-snapshot
Using Radial Basis Function Networks for Function Approximation and Classification
inferred← citeshttps://doi.org/10.7287/peerj-cs.436v0.1/reviews/1openalex-snapshot
Peer Review #1 of "Extracting automata from neural networks using active learning (v0.1)"
inferred← citeshttps://doi.org/10.7287/peerj-cs.436v0.1/reviews/2openalex-snapshot
Peer Review #2 of "Extracting automata from neural networks using active learning (v0.1)"
inferred← citeshttps://doi.org/10.7287/peerj-cs.436v0.2/reviews/1openalex-snapshot
Peer Review #1 of "Extracting automata from neural networks using active learning (v0.2)"
inferred← citeshttps://doi.org/10.7717/peerj-cs.436openalex-snapshot
Extracting automata from neural networks using active learning
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.