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fminsearch function  (MathWorks Inc)


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    MathWorks Inc fminsearch function
    Model fitting results showing the comparison between the predicted TB incidence (solid curve) and the normalized observed data (dots). The model was calibrated by minimizing the sum of squared errors (SSE) between the model output and the data using a least-squares approach. The system of ordinary differential equations was solved using MATLAB's ode45 solver, and parameter optimization was carried out using MATLAB's <t>fminsearch</t> function based on the Nelder–Mead simplex method.
    Fminsearch Function, 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/fminsearch+function/pmc12175709-199-19-19
    Average 90 stars, based on 1 article reviews
    fminsearch function - by Bioz Stars, 2026-10
    90/100 stars

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    1) Product Images from "Modelling the potential impact of TB-funded prevention programs on the transmission dynamics of TB"

    Article Title: Modelling the potential impact of TB-funded prevention programs on the transmission dynamics of TB

    Journal: Infectious Disease Modelling

    doi: 10.1016/j.idm.2025.05.010

    Model fitting results showing the comparison between the predicted TB incidence (solid curve) and the normalized observed data (dots). The model was calibrated by minimizing the sum of squared errors (SSE) between the model output and the data using a least-squares approach. The system of ordinary differential equations was solved using MATLAB's ode45 solver, and parameter optimization was carried out using MATLAB's fminsearch function based on the Nelder–Mead simplex method.
    Figure Legend Snippet: Model fitting results showing the comparison between the predicted TB incidence (solid curve) and the normalized observed data (dots). The model was calibrated by minimizing the sum of squared errors (SSE) between the model output and the data using a least-squares approach. The system of ordinary differential equations was solved using MATLAB's ode45 solver, and parameter optimization was carried out using MATLAB's fminsearch function based on the Nelder–Mead simplex method.

    Techniques Used: Comparison

    Related Articles

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    Article Title: Dissociable Effects of Curiosity and Hedonic Valence on Reinforcement Learning
    Article Snippet: The parameters of all models used were optimized using the fminsearch function in MATLAB.

    Article Title: Modelling the potential impact of TB-funded prevention programs on the transmission dynamics of TB
    Article Snippet: The system of ordinary differential equations was solved using MATLAB's ode45 solver, and parameter optimization was carried out using MATLAB's fminsearch function based on the Nelder–Mead simplex method.

    Article Title: Combined assessment of auditory distance perception and externalization.
    Article Snippet: This study investigates frontal auditory distance perception (ADP) and externalization in virtual audio-visual environments, considering effects of headphone rendering method, room size, reverberation, and visual representation of the room.. Either head-related impulse responses from an artificial head or a spherical head model were used for diotic and binaural headphone auralizations, with and without real-time head tracking.. The visuals were generated by a computer game engine and presented through a head-mounted display.

    Article Title: Supplemental information Lamellipodia dynamics andmicrorheology in endothelial cell paracellu- lar gap closure
    Article Snippet: Minimization of the error function was performed using the “fminsearch” MATLAB function and the parameters E0, α, and μ (described in the main text) were determined from this process.

    Whisker Assay:

    Article Title:
    Article Snippet: .. The optimization algorithm used in Elastica3D is the Nelder-Mead algorithm, provided by the fminsearch function in MATLAB, and the cost function of the optimization is the Euclidean distance from the location of the point force on the deflected whisker to the user-specified contact point location. ..

    Concentration Assay:

    Article Title: Role of Interfacial Processes in Accelerated Reactions in Nano- and Microdroplets.
    Article Snippet: .. The reaction rate coefficients were optimized by minimizing the sum of squared residuals between the experimental and simulated concentration/radius−time data using MATLAB’s fminsearch function. ..

    Article Title: Role of Interfacial Processes in Accelerated Reactions in Nano- and Microdroplets
    Article Snippet: .. The reaction rate coefficients were optimized by minimizing the sum of squared residuals between the experimental and simulated concentration/radius–time data using MATLAB’s fminsearch function. ..



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    Model fitting results showing the comparison between the predicted TB incidence (solid curve) and the normalized observed data (dots). The model was calibrated by minimizing the sum of squared errors (SSE) between the model output and the data using a least-squares approach. The system of ordinary differential equations was solved using MATLAB's ode45 solver, and parameter optimization was carried out using MATLAB's <t>fminsearch</t> function based on the Nelder–Mead simplex method.
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    Model fitting results showing the comparison between the predicted TB incidence (solid curve) and the normalized observed data (dots). The model was calibrated by minimizing the sum of squared errors (SSE) between the model output and the data using a least-squares approach. The system of ordinary differential equations was solved using MATLAB's ode45 solver, and parameter optimization was carried out using MATLAB's <t>fminsearch</t> function based on the Nelder–Mead simplex method.
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    Image Search Results


    Model fitting results showing the comparison between the predicted TB incidence (solid curve) and the normalized observed data (dots). The model was calibrated by minimizing the sum of squared errors (SSE) between the model output and the data using a least-squares approach. The system of ordinary differential equations was solved using MATLAB's ode45 solver, and parameter optimization was carried out using MATLAB's fminsearch function based on the Nelder–Mead simplex method.

    Journal: Infectious Disease Modelling

    Article Title: Modelling the potential impact of TB-funded prevention programs on the transmission dynamics of TB

    doi: 10.1016/j.idm.2025.05.010

    Figure Lengend Snippet: Model fitting results showing the comparison between the predicted TB incidence (solid curve) and the normalized observed data (dots). The model was calibrated by minimizing the sum of squared errors (SSE) between the model output and the data using a least-squares approach. The system of ordinary differential equations was solved using MATLAB's ode45 solver, and parameter optimization was carried out using MATLAB's fminsearch function based on the Nelder–Mead simplex method.

    Article Snippet: The system of ordinary differential equations was solved using MATLAB's ode45 solver, and parameter optimization was carried out using MATLAB's fminsearch function based on the Nelder–Mead simplex method.

    Techniques: Comparison