Fuzzy Automata and Languages: Theory and Applications
Bibliographic record
Abstract
INTRODUCTION Sets Relations Functions Fuzzy Subsets Semigroups Finite-State Machines Finite State Automata Languages and Grammars Nondeterministic Finite-State Automata Relationships Between Languages and Automata Pushdown Automata MAX-MIN AUTOMATA Max-Min Automata General Formulation of Automata Classes of Automata Behavior of Max-Min Automata Equivalences and Homomorphisms of Max-Min Automata Reduction of Max-Min Automata Definite Max-Min Automata Reduction of Max-Min Machines Equivalences Irreducibility and Minimality Nondeterministic and Deterministic Case FUZZY MACHINES, LANGUAGES, AND GRAMMARS Max-Product Machines Equivalences Irreducibility and Minimality Max-Product Grammars and Languages Weak Regular Max-Product Grammars Weak Regular Max-Product Languages Properties of GBP Exercises FUZZY LANGUAGES AND GRAMMARS Fuzzy Languages Types of Grammars Fuzzy Context-Free Grammars Context-Free Max-Product Grammars Context-Free Fuzzy Languages On the Description of the Fuzzy Meaning of Context-Free Languages Trees and Pseudoterms Fuzzy Dendrolanguage Generating Systems Normal Form of F-CFDS Sets of Derivation Trees of Fuzzy Context-Free Grammars Fuzzy Tree Automaton Fuzzy Tree Transducer Fuzzy Meaning of Context-Free Languages PROBABILISTIC AUTOMATA AND GRAMMARS Probabilistic Automata and their Approximation e-Approximating by Nonprobability Devices e-Approximating by Finite Automata Applications The Pe Relation Fuzzy Stars Acceptors and Probabilistic Acceptors Characterizations and the Re -Relation Probabilistic and Weighted Grammars Probabilistic and Weighted Grammars of Type 3 Interrelations with Programmed and Time-variant Grammars Probabilistic Grammars and Automata Probabilistic Grammars Weakly Regular Grammars and Asynchronous Automata Type-0 Probabilistic Grammars and Probabilistic Turing Machines Context-Free Probabilistic Grammars and Pushdown Automata Realization of Fuzzy Languages by Various Automata Properties of Lk, k - 1,2,3 Further Properties of L3 ALGEBRAIC FUZZY AUTOMATA THEORY Fuzzy Finite State Machines Semigroups of Fuzzy Finite State Machines Homomorphisms Admissible Relation Fuzzy Transformation Semigroups Products of Fuzzy Finite State Machines Submachines of a Fuzzy Finite State Machine Retrievability, Separability, and Connectivity Decomposition of Fuzzy Finite State Machines Subsystems of a Fuzzy Finite State Machine Strong Subsystems Cartesian Composition of Fuzzy Finite State Machines Cartesian Composition Admissible Partitions Coverings of Products of Fuzzy Finite State Machines Associative Properties of Products Covering Properties of Products Fuzzy Semiautomaton over a Finite Group MORE ON FUZZY LANGUAGES Fuzzy Regular Languages On Fuzzy Recognizers Minimal Fuzzy Recognizers Fuzzy Recognizers and Recognizable Sets Operation on (Fuzzy) Subsets Construction of Recognizers and Recognizable Sets Accessible and Coaccessible Recognizers Complete Fuzzy Machines Fuzzy Languages on a Free Monoid Algebraic Character and Properties of Fuzzy Regular Languages Deterministic Acceptors of Regular Fuzzy Languages MINIMIZATION OF FUZZY AUTOMATA Equivalence, Reduction, and Minimization of Finite Fuzzy Automata Equivalence of Fuzzy Automata: An Algebraic Approach Reduction and Minimization of Fuzzy Automata Minimal Fuzzy Finite State Automata Behavior, Reduction, and Minimization of Finite L-Automata Matrices over a Bounded Chain Systems of Linear Equivalences over a Bounded Chain Finite L-Automata-Behavior Matrix e-Equivalence e-Irreducibility Minimization L-FUZZY AUTOMATA, GRAMMARS, AND LANGUAGES Fuzzy Recognition of Fuzzy Languages Fuzzy Languages Fuzzy Recognition by Machines Cutpoint Languages Fuzzy Languages not Fuzzy Recognized by Machines in DT2 Rational Probabilistic Events Recursive Fuzzy Languages Closure Properties Fuzzy Grammars and Recursively Enumerable Fuzzy Languages Recursively Enumerable L-Subsets Various Kind of Automata with Weights APPLICATIONS A Formulation of Fuzzy Automata and its Application as a Model of Learning Systems Formulation of Fuzzy Automata Special Cases of Fuzzy Automata Fuzzy Automata as Models of Learning Systems Applications and Simulation Results Properties of Fuzzy Automata Fractionally Fuzzy Grammars with Application to Pattern Recognition Fractionally Fuzzy Grammars A Pattern Recognition Experiment General Fuzzy Acceptors for Syntactic Pattern Recognition e-Equivalence by Inputs Fuzzy-State Automata: Their Stability and Fault Tolerance Relational Description of Automata Fuzzy-State Automata Stable and Almost Stable Behavior of Fuzzy-State Automata Fault Tolerance of Fuzzy-State Automata Clinical Monitoring with Fuzzy Automata Fuzzy Systems REFERENCES INDEX Each chapter also includes a section of exercises
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".