Universiti Putra Malaysia Institutional Repository
metadata only
An optimized logic mining method for data processing through higher-order satisfiability representation in discrete hopfield neural network
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Optimizing Discrete Hopfield Neural Networks Using Major Random 1,3-Satisfiability And Hybrid Artificial Bee Colony#352752
- G-Type Random 3 Satisfiability With Discrete Hopfield Neural Network#352755
- Position: Certified Correctness in Neural Constraint Reasoning Requires Symbolic Integration#610072
- Position: Certified Correctness in Neural Constraint Reasoning Requires Symbolic Integration#609010
- Efficient allocation with ordinal preference intensities#144160
Record · ID 306298
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive