Repositorio de Universidad Autónoma de Chile.
metadata only
Construction and Supervised Learning of Long-Term Grey Cognitive Networks
Modeling a real-world system by means of a neural model involves numerous challenges that range from formulating transparent knowledge representations to obtaining reliable simulation errors. However, that knowledge is often difficult to formalize in a precise way using crisp numbers. In this paper,
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
- Environmental Triggers and Ocular Disease Networks: Analyzing the Impact of Air Pollutants and Meteorological Factors Using Fuzzy Cognitive Maps#253672
- Brass-for-Brains: Analog Computation for Cognition#146669
- Artificial neuro-fuzzy intelligence for dynamic tracking and prediction of HIV prevalence and new infections among individuals aged 15 to 49 years in Uganda: Computational national-level models.#1054324
- Cortical knowledge structures guide word concept learning.#381024
- Artificial Intelligence for Predicting and Optimizing Water Quality in Aquatic Systems#665185
Record · ID 947662
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