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non-linear optimization solver function fmincon  (MathWorks Inc)


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    MathWorks Inc non-linear optimization solver function fmincon
    Non Linear Optimization Solver Function Fmincon, supplied by MathWorks Inc, 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/pm31537768-108-1-6
    Average 90 stars, based on 1 article reviews
    non-linear optimization solver function fmincon - by Bioz Stars, 2026-10
    90/100 stars

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    Software:

    Article Title: Global maps of transcription factor properties reveal threshold-based formation of DNA-bound and mobile clusters
    Article Snippet: The values of diffusivities and fraction of the population in were estimated using the Matlab optimization function fmincon.

    Article Title: Twin differences in the Minnesota Trust Game relate to neural mechanisms of suspiciousness.
    Article Snippet: We used the fmincon function in MATLAB (2018 edition) to calculate the best parameter values for each individual using maximum likelihood estimation.

    Article Title: Dual effects of dual-tasking on instrumental learning.
    Article Snippet: Models were fit to participants’ choices using maximum likelihood estimation, by minimizing the negative log likelihood using the MATLAB fmincon function.



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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