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Upgrading automation for nuclear fuel in-core management: From the symbolic generation of configurations, to the neural adaptation of heuristics
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-1-4615-2726-8openalex-snapshot
Hybrid Neural Network and Expert Systems
inferredcites →https://doi.org/10.1007/bf00114845openalex-snapshot
The induction of dynamical recognizers
inferredcites →https://doi.org/10.1016/0020-0271(72)90029-0openalex-snapshot
Computers and artificial intelligence
inferredcites →https://doi.org/10.1016/0029-5493(67)90050-7openalex-snapshot
Nuclear science and engineering
inferredcites →https://doi.org/10.1016/0893-6080(94)90014-0openalex-snapshot
Introduction to the theory of neural computation
inferredcites →https://doi.org/10.1016/0957-4174(91)90129-3openalex-snapshot
The synergism of expert system and neural network technologies
inferredcites →https://doi.org/10.1016/0957-4174(91)90130-7openalex-snapshot
Towards connectionist production systems
inferredcites →https://doi.org/10.1016/b978-1-4832-1448-1.50010-xopenalex-snapshot
On the Computational Economics of Reinforcement Learning
inferredcites →https://doi.org/10.1109/icpr.1994.576993openalex-snapshot
An integrated symbolic and neural network architecture for machine learning in the domain of nuclear engineering
inferredcites →https://doi.org/10.1109/tsmc.1985.6313371openalex-snapshot
Pattern-recognizing stochastic learning automata
inferredcites →https://doi.org/10.1145/129852.129861openalex-snapshot
Survey of expert critiquing systems
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.1989.1.3.372openalex-snapshot
Finite State Automata and Simple Recurrent Networks
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/10.1201/9780429499661openalex-snapshot
Introduction to the Theory of Neural Computation
inferredcites →https://doi.org/10.1207/s15516709cog1603_1openalex-snapshot
Forward Models: Supervised Learning with a Distal Teacher
inferredcites →https://doi.org/10.13182/nse88-a23563openalex-snapshot
Application of a Heuristic Search Method for Generation of Fuel Reload Configurations
inferredcites →https://doi.org/10.13182/nse88-a23577openalex-snapshot
The Potential for Expert System Support in Solving the Pressurized Water Reactor Fuel Shuffling Problem
inferredcites →https://doi.org/10.13182/nse89-a23630openalex-snapshot
A Knowledge-Based System for Optimization of Fuel Reload Configurations
inferredcites →https://doi.org/10.13182/nse91-a23848openalex-snapshot
Application of Knowledge-Based Methods to In-Core Fuel Management
inferredcites →https://doi.org/10.13182/nt95-1-9openalex-snapshot
In-Core Nuclear Fuel Management Optimization for Pressurized Water Reactors Utilizing Simulated Annealing
inferredcites →https://doi.org/10.3929/ethz-a-000625223openalex-snapshot
On the uniform approximation of nonlinear discrete-time fading-memory systems using neural network models
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-3-642-45321-2_14openalex-snapshot
Nuclear In-core Fuel Reload Design: The Trajectory of a Sequence of Projects
inferred← citeshttps://doi.org/10.1007/978-3-7908-1866-6_20openalex-snapshot
From FUELCON to FUELGEN: Tools for Fuel Reload Pattern Design
inferred← citeshttps://doi.org/10.1007/s10489-023-05013-5openalex-snapshot
Assessment of reinforcement learning algorithms for nuclear power plant fuel optimization
inferred← citeshttps://doi.org/10.1016/j.anucene.2021.108355openalex-snapshot
Large-scale design optimisation of boiling water reactor bundles with neuroevolution
inferred← citeshttps://doi.org/10.1016/j.nucengdes.2011.06.049openalex-snapshot
An integrated expert system for optimum in core fuel management
inferred← citeshttps://doi.org/10.1016/s0166-3615(98)00080-3openalex-snapshot
Refueling in nuclear engineering: the FUELCON project
inferred← citeshttps://doi.org/10.1016/s0957-4174(98)00004-9openalex-snapshot
Intelligent technologies for nuclear power systems: heuristic and neural tools
inferred← citeshttps://doi.org/10.1016/s0957-4174(98)00005-0openalex-snapshot
FuelGen: a genetic algorithm-based system for fuel loading pattern design in nuclear power reactors
inferred← citeshttps://doi.org/10.1145/3648608openalex-snapshot
Resilient Machine Learning: Advancement, Barriers, and Opportunities in the Nuclear Industry
inferred← citeshttps://doi.org/10.3390/designs3030037openalex-snapshot
An Overview of AI Methods for in-Core Fuel Management: Tools for the Automatic Design of Nuclear Reactor Core Configurations for Fuel Reload, (Re)arranging New and Partly Spent Fuel
inferred← citeshttps://doi.org/10.7166/27-3-1650openalex-snapshot
AN OPTIMISATION-BASED DECISION SUPPORT SYSTEM FRAMEWORK FOR MULTI-OBJECTIVE IN- CORE FUEL MANAGEMENT OF NUCLEAR REACTOR CORES
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