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non-linear solver (the fmincon function within matlab)  (MathWorks Inc)


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    MathWorks Inc non-linear solver (the fmincon function within matlab)
    Non Linear Solver (The Fmincon Function Within Matlab), 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+solver+fmincon/pmc09797521-85-4-10
    Average 90 stars, based on 1 article reviews
    non-linear solver (the fmincon function within matlab) - by Bioz Stars, 2026-09
    90/100 stars

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    other:

    Article Title: Sensitivity analysis of pinna morphology on head-related transfer functions simulated via a parametric pinna model.
    Article Snippet: Since it was impossible to create pinna variations that were completely independent, as changing one parameter could potentially move multiple control points, a non-linear solver (fmincon in MATLAB) was used to generate pinnae with parameters as close as possible to the desired values for the spatial average reference pinna and all the minimum/ maximum variations.

    Article Title: Joint modelling of diffusion MRI and microscopy
    Article Snippet: The three error terms were combined into a single cost function (Eq.), with regularisation factors λ m i c r o and λ c o m p l e x respectively, and minimised using MATLAB’s non-linear solver fmincon ( ).

    Article Title: Distributed control scheme for residential battery energy storage units coupled with PV systems
    Article Snippet: The opti-311 mization problem in the local scheduling algorithm was solved using dynamic programming [34], whereas the312 optimization problems in centralized control of BES units and the reactive power control of converters were313 solved using the Matlab non-linear solver ”fmincon”.



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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