Repositorio de Universidad Autónoma de Chile.
metadata only
Elliot and symmetric elliot extreme learning machines for Gaussian noisy industrial thermal modelling
This research proposes an Elliot-based Extreme Learning Machine approach for industrial thermal processes regression. The main contribution of this paper is to propose an Extreme Learning Machine model with Elliot and Symmetric Elliot activation functions that will look for the fittest number of neu
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
- IC-ThermBench: An Open, Progressive Benchmark for Generalizable 2.5D/3D-IC Thermal Learning#974563
- HYBRID DEEP LEARNING FRAMEWORKS FOR WEATHER PREDICTION AND ENERGY DEMAND FORECASTING#1016122
- Editorial: Smart forecasting: deep learning and explainable AI for real-world time series prediction.#1091294
- A Rapid Physics-Informed Machine Learning Framework Based on Extreme Learning Machine for Inverse Stefan Problems.#489522
- Data for: A Physics-Regularized Machine Learning Approach for Predicting Time–Temperature–Transformation Curves in Alloys: Application to Uranium-Based Alloys#1042074
Record · ID 936038
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