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rbf neural network algorithm  (MathWorks Inc)


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    MathWorks Inc rbf neural network algorithm
    Rbf Neural Network Algorithm, 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/pmc08519688-214-5-18
    Average 90 stars, based on 1 article reviews
    rbf neural network algorithm - by Bioz Stars, 2026-10
    90/100 stars

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    other:

    Article Title: Proposing Intelligent Approach to Predicting Air Kerma within Radiation Beams of Medical X-ray Imaging Systems.
    Article Snippet: In this research, MATLAB software was used for training and testing the RBF neural network.

    Article Title: Novel Hybrid Modeling and Analysis Method for Steam Reforming Solid Oxide Fuel Cell System Multifault Degradation Fusion Assessment
    Article Snippet: In MATLAB software, the function newrbe is used to create an RBF neural network, and the second group of data is used to test the network and adjust the network precision SPREAD value.

    Article Title: Decanoic acid-palmitic acid/SiO 2 @TiO 2 phase change microcapsules based on RBF model with excellent photocatalysis performance and humidity control property.
    Article Snippet: The decanoic acid-palmitic acid composite phase change material compounds with SiO2 and TiO2 to prepare decanoic acidpalmitic acid/SiO2@TiO2 phase change microcapsules (D-P-SiO2@TiO2 PCM).. The D-P-SiO2@TiO2 PCM could show efficient temperature regulation, remove pollutants through photocatalysis, and control air humidity.. However, it is difficult to obtain the best experimental scheme directly using the traditional experimental setup due to the complicated photocatalytichumidity performance.

    Construct:

    Article Title: Study of BP and RBF Neural Networks Applied to the Prediction of Vibration Characteristics in Static Blasting of Dry Ice Powder
    Article Snippet: .. Using the five parameters of hole depth, blast center distance, dry ice powder mass and rock classification as the main influencing factors, BP and RBF neural network models were constructed by Matlab software to predict the peak vibration velocity, main frequency and maximum displacement of dry ice powder blasting. ..

    Article Title: Farmland reference crop evapotranspiration prediction method considering uncertainty of meteorological factors
    Article Snippet: .. When implemented, preferably, the RBF neural network is constructed through an RBF toolbox of matrix laboratory software (developed by MathWorks and sold under the trademark MATLAB), and a call format of the specific function is: net=newrb(P,T,goal,spread,MN,DF); wherein P and T are input and output variables, respectively; goal is a mean square error, goal=0.001; spread is a width of the RBF; MN is a maximum number of neurons during an artificial neural network (ANN) learning process; and DF is a number of neurons added between two displays. ..

    Article Title: Prediction of NO concentration using modular long short-term memory neural network for municipal solid waste incineration
    Article Snippet: Article history: Received 18 December 2021 Received in revised form 18 May 2022 Accepted 6 June 2022 Available online 1 August 2022

    Software:

    Article Title: Farmland reference crop evapotranspiration prediction method considering uncertainty of meteorological factors
    Article Snippet: .. When implemented, preferably, the RBF neural network is constructed through an RBF toolbox of matrix laboratory software (developed by MathWorks and sold under the trademark MATLAB), and a call format of the specific function is: net=newrb(P,T,goal,spread,MN,DF); wherein P and T are input and output variables, respectively; goal is a mean square error, goal=0.001; spread is a width of the RBF; MN is a maximum number of neurons during an artificial neural network (ANN) learning process; and DF is a number of neurons added between two displays. ..



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    Image Search Results


    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