neural network toolbox matlab r2012a (MathWorks Inc)
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neural network toolbox matlab r2012a
Neural Network Toolbox Matlab R2012a, 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/neural+network+toolbox+matlab+r2012a/pmc04352926-392-12-11
Average 90 stars, based on 1 article reviews
Neural Network Toolbox Matlab R2012a, 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/neural+network+toolbox+matlab+r2012a/pmc04352926-392-12-11
Average 90 stars, based on 1 article reviews
neural network toolbox matlab r2012a - by Bioz Stars,
2026-09
90/100 stars
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Modification:Article Title: Can shoulder joint reaction forces be estimated by neural networks? Article Snippet: the mechanical load on the glenohumeral joint, in terms of joint reaction forces.. For long term ambulatory measurements, these 3D kinematics can be measured by means of Inertial Magnetic Measurement Systems.. Recording of external forces under daily conditions is not feasible; estimations of joint loading should preferably be independent of this input. Article Title: Co-combustion of peanut hull and coal blends: Artificial neural networks modeling, particle swarm optimization and Monte Carlo simulation. Article Snippet: a r t i c l e i n f o Article Title: Lung Cancer Prediction Using Neural Network Ensemble with Histogram of Oriented Gradient Genomic Features Article Snippet: The designed MLP-ANN was programmed using the neural network toolbox in Article Title: Reaction mechanisms and product patterns of Pteris vittata pyrolysis for cleaner energy Article Snippet: The pyrolysis behaviors, kinetics, evolved products, and optimization of aboveground (PA) and belowground (PB) biomass of Pteris vittata were quantified.. The pyrolysis performance in response to the elevated heating rate was improved by 21.21 and 16.79 times for PA and PB, respectively.. CH4 and CO emissions were produced more from the pyrolysis of PB than PA. Article Title: Investigation of thermal conversion characteristics and performance evaluation of co-combustion of pine sawdust and lignite coal using TGA, artificial neural network modeling and likelihood method. Article Snippet: (Co-)combustion of pine sawdust (PS) and lignite coal (LC) were investigated using artificial neural networks (ANN), particle swarm optimization (PSO), and Monte Carlo simulation (MC) as a function of blend ratio, heating rate, and temperature via thermal conversion characteristics.. The order of degraded compounds in terms of hemi-cellulosic and lignin-based compounds demonstrated the main oxidation and degradation mechanism of co-combustion of PS and LC.. The best prediction (R2 of 99.99%) was obtained by ANN28 model. Operating conditions of 90LC10PS, 425 °C, and 19 °Cmin were determined by PSO as optimum levels with TG value of 67.5%. |