Centro de Investigación Científica y de Educación Superior de Ensenada, Baja California
Año
Autores / Publicación
Villa Diharce, E., Echavarría Heras, H. A., Montesinos Lopez, A., & Leal Ramírez, C. (2022). A Revision of the Traditional Analysis Method of Allometry to Allow Extension of the Normality-Borne Complexity of Error Structure: Examining the Adequacy of a Normal-Mixture Distribution-Driven Error Term. BioMed Research International, 2022(8310213), 31. doi: 10.1155/2022/8310213. (ID: 27903)
Leal Ramírez, C., Echavarría Heras, H. A., & Romero Escobar, H. M. (2022). A Mamdani Type-Fuzzy Inference - Alignment Matrix Method for Evaluation of Competencies Acquired by Students Enrolling at the Mexican Higher Middle Education System I: Formulation and Explanation Based on Simulation, and a Real but Incomplete Data Set. Computación y Sistemas, 2(2), 571-601. doi: 10.13053/CyS-26-2-4236. (ID: 27817)
Echavarría Heras, H. A., Leal Ramírez, C., Gomez, G., & Montiel Arzate, E. (2021). Principle of Limiting Factors-Driven Piecewise Population Growth Model I: Qualitative Exploration and Study Cases on Continuous-Time Dynamics. COMPLEXITY. doi: 10.1155/2021/5623783. (ID: 27216)
Leal Ramírez, C., & Echavarría Heras, H. A. (2021). On the Adequacy of a Takagi¿Sugeno¿Kang Protocol as an Empirical Identification Tool for Sigmoidal Allometries in Geometrical Space. In Castillo Oscar Melin Patricia (Eds.), Fuzzy Logic Hybrid Extensions of Neural and Optimization Algorithms: Theory and Applications (pp. 315-336). Springer. (ID: 26896)
Leal Ramírez, C., Echavarría Heras, H. A., & Villa Diharce, E. (2020). Applying Fuzzy Logic to Identify Heterogeneity of the Allometric Response in Arithmetical Space. In Oscar Castillo Patricia Melin Janusz Kacprzyk (Eds.), Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications, Studies in Computational (pp. 11-34). Springer. (ID: 26027)
Echavarría Heras, H. A., Castro Rodriguez, J. R., Leal Ramírez, C., & Villa Diharce, E. (2020). Assessment of a Takagi-Sugeno-Kang fuzzy model assembly for examination of polyphasic loglinear allometry. PeerJ, 1(1), 50. doi: 10.7717/peerj.8173. (ID: 26026)
Echavarría Heras, H. A., Leal Ramírez, C., Villa Diharce, E., & Castro Rodriguez, J. R. (2019). A Generalized Model of Complex Allometry I: Formal Setup, Identification Procedures and Applications to Non-Destructive Estimation of Plant Biomass Units. Applied Sciences, 9(22), 42. doi: 10.3390/app9224965. (ID: 25144)
Echavarría Heras, H. A., Leal Ramírez, C., Villa Diharce, E., & Montesinos Lopez, A. (2019). Examination of the Effects of Curvature in Geometrical Space on Accuracy of Scaling Derived Projections of Plant Biomass Units: Applications to the Assessment of Average Leaf Biomass in Eelgrass Shoots. BioMed Research International, 2019(3613679), 23. doi: 10.1155/2019/3613679. (ID: 25146)
Montesinos Lopez, A., Villa Diharce, E., Echavarría Heras, H. A., & Leal Ramírez, C. (2018). Improved allometric proxies for eelgrass conservation. Journal of Coastal Conservation, 21. doi: 10.1007/s11852-018-0639-4. (ID: 23866)
Cazarez Castro, N. R., Odreman Vera, M., Cardenas Maciel, S., Echavarría Heras, H. A., & Leal Ramírez, C. (2018). Fuzzy Differential Equations as a Tool for Teaching Uncertainty in Engineering and Science. Computación y Sistemas, 22(2), 11. doi: 10.13053/CyS-22-2-2947. (ID: 23867)
Echavarría Heras, H. A., Leal Ramírez, C., Castro Rodriguez, J. R., Villa Diharce, E., & Castillo, O. (2018). A Takagi¿Sugeno-Kang Fuzzy Model Formalization of Eelgrass Leaf Biomass Allometry with Application to the Estimation of Average Biomass of Leaves in Shoots: Comparing the Reproducibility Strength of the Present Fuzzy and Related Crisp Proxies. In Oscar Castillo, Patricia Melin and Janusz Kacprzyk (Eds.), Fuzzy Logic Augmentation of Neural and Optimization Algorithms: Theoretical Aspects and Real Applications (2da Edicion. ed., pp. 329-362). Springer. (ID: 23868)
Echavarría Heras, H. A., Leal Ramírez, C., Villa Diharce, E., & Cazarez Castro, N. R. (2018). On the suitability of an allometric proxy for nondestructive estimation of average leaf dry weight in eelgrass shoots I: sensitivity analysis and examination of the influences of data quality, analysis method, and sample size on precision. Theoretical Biology and Medical Modelling, 15(4), 20. doi: 10.1186/s12976-018-0076-y. (ID: 23865)
Leal Ramírez, C., Echavarría Heras, H. A., Castillo, O., & Montiel Arzate, E. (2016). On the Use of Parallel Genetic Algorithms for Improving the Efficiency of a Monte Carlo-Digital Image Based Approximation of Eelgrass Leaf Area I: Comparing the Performances of Simple and Master-Slaves Structures. In Patricia Melin, Oscar Castillo, Janusz Kacprzyk (Eds.), Nature-Inspired Design of Hybrid Intelligent Systems. Volume 667 of the series Studies in Computational Intelligence (pp. 431-455). Springer. (ID: 21486)
Echavarría Heras, H. A., Leal Ramírez, C., Villa Diharce, E., & Cazarez Castro, N. R. (2015). The effect of parameter variability in the allometric projection of leaf growth rates for eelgrass (Zostera marina L.) II: the importance of data quality control procedures in bias reduction. Theoretical Biology and Medical Modelling, 12(30). doi: 10.1186/s12976-015-0025-y. (ID: 19518)
Leal Ramírez, C., Echavarría Heras, H. A., & Castillo, O. (2015). Exploring the Suitability of a Genetic Algorithm as Tool for Boosting Efficiency in Monte Carlo Estimation of Leaf Area of Eelgrass. In Patricia Melin, Oscar Castillo, Janusz Kacprzyk (Eds.), Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization. Studies in Computational Intelligence (Vol. 601, pp. 291-303). Springer. (ID: 19288)