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non-linear least square solver matlab optimization toolbox  (MathWorks Inc)


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    MathWorks Inc non-linear least square solver matlab optimization toolbox
    Non Linear Least Square Solver Matlab Optimization Toolbox, 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/pm31447373-183-28-32
    Average 90 stars, based on 1 article reviews
    non-linear least square solver matlab optimization toolbox - by Bioz Stars, 2026-10
    90/100 stars

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    Article Title: Intracellular oxygen transient quantification in vivo during Ultra-High Dose Rate (UHDR) FLASH radiotherapy
    Article Snippet: In the same way, the relationship between PpIX ratiometric measurements and PdG4 based measurements were fitted using a linear model. All models were fitted using an iterative non-linear least square method (Matlab R2018a, The MathWorks Inc., Natick, MA).

    Article Title: Characterization of Directional Reflectance in Corneal Tissue: A Comprehensive Optical Coherence Tomography Analysis.
    Article Snippet: Curve fitting was performed using non-linear least squares optimization in MATLAB R2023b (MathWorks Inc., Natick, MA, USA).

    Article Title: A rationally thin composite membrane with differentiated pore structure for industrial-scale alkaline water electrolysis.
    Article Snippet: Themorphologyof PMCMwasobservedwith a scanning electron microscope (Regulus-8100, HITACHI, Japan) after drying in a freeze dryer for 24 h and under gold plating conditions for 60 s. The surface roughness of PMCM was obtained by atomic force microscopy (Dimension Icon, Bruker, Germany) measurements.

    Article Title: A Toolbox for the Personalization of Plasmonic Photothermal Therapy in Orthotopic Mouse Tumor Models
    Article Snippet: In the chosen approach, each variable was fitted roughly in the wavelength range were they had a higher impact on the reflectance, while leaving the rest of parameters constant, using a non-linear least-squares algorithm in MATLAB software (Version 9.5.0.1067069 (R2018b) Update 4, The Mathworks, Inc.).

    Article Title: A Finely Balanced Order-Disorder Equilibrium Sculpts the Folding-Binding Landscape of an Antibiotic Sequestering Protein
    Article Snippet: The experimental heat capacity curve was employed to calibrate the model parameters through non-linear least-squares fitting procedures in MATLAB.

    Article Title: Imaging Behind the Plaque: Improved Blood Flow Quantification Using an Iterative Scheme for Active Attenuation Correction.
    Article Snippet: To prevent large error accumulation, the integral error EI was first clipped such that Preq - Ep remained approximately between P0 and P1 (obtained from eqn [3]) using ae 1∀e∈ 1;N . The ‘Minimize’ step in Fig. 1b used a non-linear least-squares method (MATLAB’s lsqcurvefit) to find A such that the function Y below was minimized.

    Article Title:
    Article Snippet: To fit this data to our active extrusion model (see Fig. 5 in main text), non-linear least-squares fitting using MATLAB was performed on this data to I ≈ eC1 ∗ P(N) + C2 , (S24) where P(N) is given by Eq. (S8).

    Article Title: A complex interplay between alginate/pectin and rice varieties in determining the starch digestibility of reconstituted rice beads.
    Article Snippet: Non-starch polysaccharide (NSP) encapsulated rice flour is a novel type of rice analogue, while the influence of different NSPs and rice varieties on starch digestibility remains unknown.. Therefore, three rice varieties with different structures were encapsulated by alginate or pectin, and their digestibility and textures were investigated with regards to the alterations of their multi-scale structures during digestion.. An approximately 1.9 times difference of enzymatic binding rate constant and 10 % difference of the maximum starch digested percentage were found among different rice beads.



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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