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Nibaldo Rodriguez

Researcher at Pontifical Catholic University of Valparaíso

Publications -  70
Citations -  567

Nibaldo Rodriguez is an academic researcher from Pontifical Catholic University of Valparaíso. The author has contributed to research in topics: Autoregressive model & Wavelet. The author has an hindex of 11, co-authored 70 publications receiving 499 citations. Previous affiliations of Nibaldo Rodriguez include Valparaiso University.

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Monthly catch forecasting of anchovy Engraulis ringens in the north area of Chile: Non-linear univariate approach

TL;DR: In this article, the performance of computational neural networks (CNNs) models to forecast 1-month ahead monthly anchovy catches in the north area of Chile considering only anchovy catch in previous months as inputs to the models was analyzed.
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Collaborative particle swarm optimization with a data mining technique for manufacturing cell design

TL;DR: The computational results show that the PSO algorithm is able to find the optimal solutions in almost all instances, and its use in machine grouping problems is feasible.
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Neural networks for cost estimation of shell and tube heat exchangers

TL;DR: This model proved that neural networks are capable of reducing uncertainties related to the cost estimation of a shell and tube heat exchangers through the application of artificial neural networks.
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Anchovy (Engraulis ringens) and sardine (Sardinops sagax) abundance forecast off northern Chile: A multivariate ecosystemic neural network approach

TL;DR: In this article, an evaluation of the performance of artificial neural networks (ANNs) to forecast monthly anchovy (Engraulis ringens) and sardine (Sardinops sagax) catches in northern Chile (18°21′S-24°S) is presented, using environmental variables, including anchovy and Sardine CPUE, fishing effort and catches.
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Application of Genetic Algorithms for the DARPTW Problem

TL;DR: This work applies data pre-processing techniques to reduce the search space to points that are feasible regarding time windows constraints, and shows competitive results on Cordeau & Laporte benchmark datasets while improving processing times.