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matlab yalmip toolbox  (MathWorks Inc)


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    MathWorks Inc matlab yalmip toolbox
    Matlab Yalmip Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1226 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/matlab+control+systems+toolbox/Control+System+Toolbox/pm41904903-231-14-14
    Average 96 stars, based on 1226 article reviews
    matlab yalmip toolbox - by Bioz Stars, 2026-10
    96/100 stars

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    Control:

    Article Title: Feedback flow control employing local dynamical modelling with wavelets
    Article Snippet: .. For the problem at hand, numerous compensators of different orders were designed using these methods with the help of MATLAB Control Systems Toolbox. ..

    Article Title: Local Operators and Quantum Chaos
    Article Snippet: .. These are provided, for example, in the MATLAB Control Systems Toolbox as balred, imp2ss and in the SLICOT library 20 as AB09AD. ..

    Article Title: Flexible strategies for flight control: an active role for the abdomen.
    Article Snippet: .. We derived the equations of motion for a simplified model of a moth (Fig.1C) and analyzed the effectiveness of the behaviorally measured transfer function using the Mathematica software package (Wolfram Research, Champaign, IL, USA) and the MATLAB Control Systems toolbox. ..

    Article Title: MEMS Lens Scanners for Free-Space Optical Interconnects
    Article Snippet: .. The discrete-time PID controller was designed using the MATLAB Control Systems Toolbox through a constrained optimization procedure. ..

    Article Title: Feedback methods for inverse simulation of dynamic models for engineering systems applications
    Article Snippet: .. Taking the desired pole positions for the feedback system as the positions of the zeros of the forward model together with two additional poles at s = −13 and s = −14, an appropriate state-variable feedback structure can be found without difficulty in using, for example, the place function within the MATLAB® Control Systems Toolbox, The Mathworks Inc., Natick, MA, USA [36]. ..

    Derivative Assay:

    Article Title: Flexible strategies for flight control: an active role for the abdomen.
    Article Snippet: .. We derived the equations of motion for a simplified model of a moth (Fig.1C) and analyzed the effectiveness of the behaviorally measured transfer function using the Mathematica software package (Wolfram Research, Champaign, IL, USA) and the MATLAB Control Systems toolbox. ..

    Software:

    Article Title: Flexible strategies for flight control: an active role for the abdomen.
    Article Snippet: .. We derived the equations of motion for a simplified model of a moth (Fig.1C) and analyzed the effectiveness of the behaviorally measured transfer function using the Mathematica software package (Wolfram Research, Champaign, IL, USA) and the MATLAB Control Systems toolbox. ..



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    (A) Experimental paradigm. We analyzed EEG data recorded from 49 sleeping human newborns while being exposed to monophonic piano melodies composed by J. S. Bach (real condition) and control stimuli (shuffled condition). (B) Surprise and entropy. Surprise and entropy associated with each note’s timing (green, St and Et, respectively) and pitch (yellow, Sp and Ep, respectively) were estimated using an unsupervised statistical learning model trained on all stimuli. Dot plots display mean surprise and entropy associated with real and shuffled music, averaged across melodies (left panel), and separately for each melody (right panel). Error bars represent bootstrapped 95% confidence intervals (CI). See . (C) Correlations between stimulus features. Pearson’s correlations ( r values) between the stimulus features: inter-pitch-interval (IPI), inter-onset-interval (IOI), and surprise and entropy associated with timing (St and Et) and pitch (Sp and Ep). See . (D) Analytical approach. Multivariate Temporal Response Function (mTRF) models were fit to describe the forward relationship between multiple stimulus features and the EEG signal. The full TRF model (leftmost panel) included acoustic low-level features (spectral flux, acoustic onset, IOI, and IPI) and high-level features (surprise and entropy of pitch and timing). To assess the unique contribution of each feature (or set of features) to the EEG data, we run reduced models encompassing all variables but with the specified one being randomized in time (yet preserving the note onset times). We then calculated the difference in EEG prediction accuracy (Pearson’s correlations, r ) between the reduced models and the full model (Δr). On the rightmost panel, the light blue circle denotes information of a reduced model, with the variable(s) of interest being randomized. The orange area indicates the unique contribution of the variable of interest that leads to an increase in the explanatory power of the full model (black circle).

    Journal: PLOS Biology

    Article Title: Human newborns form musical predictions based on rhythmic but not melodic structure

    doi: 10.1371/journal.pbio.3003600

    Figure Lengend Snippet: (A) Experimental paradigm. We analyzed EEG data recorded from 49 sleeping human newborns while being exposed to monophonic piano melodies composed by J. S. Bach (real condition) and control stimuli (shuffled condition). (B) Surprise and entropy. Surprise and entropy associated with each note’s timing (green, St and Et, respectively) and pitch (yellow, Sp and Ep, respectively) were estimated using an unsupervised statistical learning model trained on all stimuli. Dot plots display mean surprise and entropy associated with real and shuffled music, averaged across melodies (left panel), and separately for each melody (right panel). Error bars represent bootstrapped 95% confidence intervals (CI). See . (C) Correlations between stimulus features. Pearson’s correlations ( r values) between the stimulus features: inter-pitch-interval (IPI), inter-onset-interval (IOI), and surprise and entropy associated with timing (St and Et) and pitch (Sp and Ep). See . (D) Analytical approach. Multivariate Temporal Response Function (mTRF) models were fit to describe the forward relationship between multiple stimulus features and the EEG signal. The full TRF model (leftmost panel) included acoustic low-level features (spectral flux, acoustic onset, IOI, and IPI) and high-level features (surprise and entropy of pitch and timing). To assess the unique contribution of each feature (or set of features) to the EEG data, we run reduced models encompassing all variables but with the specified one being randomized in time (yet preserving the note onset times). We then calculated the difference in EEG prediction accuracy (Pearson’s correlations, r ) between the reduced models and the full model (Δr). On the rightmost panel, the light blue circle denotes information of a reduced model, with the variable(s) of interest being randomized. The orange area indicates the unique contribution of the variable of interest that leads to an increase in the explanatory power of the full model (black circle).

    Article Snippet: We employed Temporal Response Functions (TRF) to model EEG responses to the continuous acoustic and musical features of the presented stimuli using the mTRF MATLAB toolbox [ ].

    Techniques: Control, Preserving