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optimization function of non-linear least squares solver  (Insightful Corp)

 
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    Insightful Corp optimization function of non-linear least squares solver
    Optimization Function Of Non Linear Least Squares Solver, supplied by Insightful Corp, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/non-linear+optimization+solver/optimization+function+of+non+linear+least+squares+solver/10__1016_slash_j__agrformet__2003__10__002-188-16-25
    Average 90 stars, based on 1 article reviews
    optimization function of non-linear least squares solver - by Bioz Stars, 2026-09
    90/100 stars

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    other:

    Article Title: In situ comparison of four approaches to estimating soil CO2 efflux in a northern larch (Larix kaempferi Sarg.) forest
    Article Snippet: Soil surface CO2 efflux (Fc) in a 45-year-old northern larch forest was continuously measured, using a multichannel automated chamber system, between June and October, 2001.. The results were compared with periodic measurements obtained at the same site with an LI-6400 chamber system and an open-top chamber system, and also with continuous measurements obtained at the same site with a soil CO2 gradient system.. The diurnal and seasonal changes in Fc measured by the automated chamber and soil CO2 gradient approaches followed the soil temperature patterns.



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    Trained Belief Rule Base Using fmincon for Patient Survival Probability (Y) where input antecedents are X1: Patient Condition, X2: Blood Pressure, X3: Chronic Obstructive Pulmonary Disease, X4: Blood Sugar, X5: Asthma, X6: Chronic Kidney Disease, X7: Obesity, X8: Acute Respiratory Distress Syndrome, X9: Pulse Oximetry, Critical: C, Non Critical: NC, Hypertensive Crisis: HC, Very Severely Abnormal: VSA, Diabetic: D, Severe Persistent: SP, Very Severe: VS, Level III: L-III, Severe: S, Moderate: M, Normal: N, Mild: Mi, VH: Very High, H: High, M: Medium, L: Low, VL: Very Low)

    Journal: Cognitive Computation

    Article Title: An Integrated Deep Learning and Belief Rule Base Intelligent System to Predict Survival of COVID-19 Patient under Uncertainty

    doi: 10.1007/s12559-021-09978-8

    Figure Lengend Snippet: Trained Belief Rule Base Using fmincon for Patient Survival Probability (Y) where input antecedents are X1: Patient Condition, X2: Blood Pressure, X3: Chronic Obstructive Pulmonary Disease, X4: Blood Sugar, X5: Asthma, X6: Chronic Kidney Disease, X7: Obesity, X8: Acute Respiratory Distress Syndrome, X9: Pulse Oximetry, Critical: C, Non Critical: NC, Hypertensive Crisis: HC, Very Severely Abnormal: VSA, Diabetic: D, Severe Persistent: SP, Very Severe: VS, Level III: L-III, Severe: S, Moderate: M, Normal: N, Mild: Mi, VH: Very High, H: High, M: Medium, L: Low, VL: Very Low)

    Article Snippet: In order to ensure the reliability of the rules in the initial BRB, the BRBES is trained using the non-linear optimization solver fmincon in MATLAB optimization toolbox [ ], Belief Rule-Based Adaptive Particle Swarm Optimization (BRBAPSO) [ ], and the enhanced Belief Rule-Based adaptive Differential Evolution (eBRBaDE) algorithm [ ].

    Techniques:

    Comparison of AUC of CNN-BRBES and Expert Opinion

    Journal: Cognitive Computation

    Article Title: An Integrated Deep Learning and Belief Rule Base Intelligent System to Predict Survival of COVID-19 Patient under Uncertainty

    doi: 10.1007/s12559-021-09978-8

    Figure Lengend Snippet: Comparison of AUC of CNN-BRBES and Expert Opinion

    Article Snippet: In order to ensure the reliability of the rules in the initial BRB, the BRBES is trained using the non-linear optimization solver fmincon in MATLAB optimization toolbox [ ], Belief Rule-Based Adaptive Particle Swarm Optimization (BRBAPSO) [ ], and the enhanced Belief Rule-Based adaptive Differential Evolution (eBRBaDE) algorithm [ ].

    Techniques: Comparison