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backpropagation learning algorithms based on mlp and radius-based functions (rbf)  (MathWorks Inc)


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    MathWorks Inc backpropagation learning algorithms based on mlp and radius-based functions (rbf)
    Backpropagation Learning Algorithms Based On Mlp And Radius Based Functions (Rbf), 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/rbf+algorithm/10__1016_slash_j__cscee__2024__100725-80-9-17
    Average 90 stars, based on 1 article reviews
    backpropagation learning algorithms based on mlp and radius-based functions (rbf) - by Bioz Stars, 2026-11
    90/100 stars

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    Article Title: Prognosis of cervical myelopathy based on diffusion tensor imaging with artificial intelligence methods.
    Article Snippet: NMR in Biomedicine.. 2019;e4114. https://doi.org/10.1002/nbm.4114 Diffusion tensor imaging (DTI) has been proposed for the prognosis of cervical myelopathy (CM), but the manual analysis of DTI features is complicated and time consuming.. This study evaluated the potential of artificial intelligence (AI) methods in the analysis of DTI for the prognosis of CM.

    Article Title: Comparison of optimization algorithms for parameter estimation of multi-phase flow models with application to geological carbon sequestration
    Article Snippet: Optimization of multi-phase transport models is important both for calibrating model parameters to observed data and for analyzing management options.. We focus on examples of geological carbon sequestration (GCS) process-based multi-phase models.. Realistic GCS models can be very computationally expensive not only due to the spatial distribution of the model but also because of the complex nonlinear multi-phase and multi-component transport equations to be solved.

    Article Title: Simulation and Prediction of Fungal Community Evolution Based on RBF Neural Network
    Article Snippet: Considering this effect, we use RBF neural network algorithm to implement the prediction model with the help of Matlab software to achieve better prediction results [ ].

    Article Title: Optimization of the hydrolysis of lignocellulosic residues by using radial basis functions modeling and particle swarm optimization
    Article Snippet: The complete RBF-PSO optimization algorithm was written in MATLAB R2008a (The MathWorks, Inc.).

    Modification:

    Article Title: A radial basis function neural network approach to determine the survival of Listeria monocytogenes in Katiki, a traditional Greek soft cheese.
    Article Snippet: .. The training subset consisted of the survival data of L. monocytogenes at 5, 10, 15, and 20 C (45 data points), whereas the generalization capability (prediction) of the network was tested on the remaining 26 data points at 12 and 17 C. The algorithm of the RBF network was developed in MATLAB version 7.0 code (Mathworks, Inc., Natick, Mass.), whereas the reparameterized Gompertz, modified Weibull, and Geeraerd models were fitted by the nonlinear regression procedure in Statistica software version 6.0 (Statsoft, Inc., Tulsa, Okla.). ..

    Software:

    Article Title: A radial basis function neural network approach to determine the survival of Listeria monocytogenes in Katiki, a traditional Greek soft cheese.
    Article Snippet: .. The training subset consisted of the survival data of L. monocytogenes at 5, 10, 15, and 20 C (45 data points), whereas the generalization capability (prediction) of the network was tested on the remaining 26 data points at 12 and 17 C. The algorithm of the RBF network was developed in MATLAB version 7.0 code (Mathworks, Inc., Natick, Mass.), whereas the reparameterized Gompertz, modified Weibull, and Geeraerd models were fitted by the nonlinear regression procedure in Statistica software version 6.0 (Statsoft, Inc., Tulsa, Okla.). ..



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    Comparison of Different FPGA  SVM  Implementation

    Journal: Ieee Transactions on Very Large Scale Integration (Vlsi) Systems

    Article Title: An Ultralow-Power Real-Time Machine Learning Based fNIRS Motion Artifacts Detection

    doi: 10.1109/TVLSI.2024.3356161

    Figure Lengend Snippet: Comparison of Different FPGA SVM Implementation

    Article Snippet: The combined fNIRS data preprocessing and RBF kernel SVM algorithm architecture were designed and simulated within the Simulink environment.

    Techniques: Comparison