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neural network toolbox version 7.0  (MathWorks Inc)


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    MathWorks Inc neural network toolbox version 7.0
    Neural Network Toolbox Version 7.0, 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+version+7%2E0/10__1080_slash_08839514__2015__1082279-134-2-11
    Average 90 stars, based on 1 article reviews
    neural network toolbox version 7.0 - by Bioz Stars, 2026-09
    90/100 stars

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    Article Title: Predictions of oil/chemical tanker main design parameters using computational intelligence techniques
    Article Snippet: Ship design and construction are complicated and expensive processes.. In the pre-design stage, before the construction according to some special rules, determination of the main ship parameters is very important.. In this study, instead of traditional methods, the oil/chemical tanker main design parameters are estimated by 18 computational intelligence methods.

    Article Title: Global optimization with multivariate adaptive regression splines.
    Article Snippet: This paper presents a novel procedure for approximating the global optimum in structural design by combining multivariate adaptive regression splines (MARS) with a response surface methodology (RSM).. MARS is a flexible regression technique that uses a modified recursive partitioning strategy to simplify high-dimensional problems into smaller yet highly accurate models.. Combining MARS and RSM improves the conventional RSM by addressing highly nonlinear high-dimensional problems that can be simplified into lower dimensions, yet maintains a low computational cost and better interpretability when compared to neural networks and generalized additive models.

    Article Title: Multiple Decision Expert Systems for Performance Analysis of a Boiler System
    Article Snippet: Multiple Decision Expert Systems for Performance Analysis of a Boiler System Saroj Kumar Meher, Shishir Kumar Behera, Min Choul Kim & Hung-Suck Park To cite this article: Saroj Kumar Meher, Shishir Kumar Behera, Min Choul Kim & Hung-Suck Park (2015) Multiple Decision Expert Systems for Performance Analysis of a Boiler System, Applied Artificial Intelligence, 29:9, 839-858, DOI: 10.1080/08839514.2015.1082279 To link to this article: http://dx.doi.org/10.1080/08839514.2015.1082279

    Article Title: Moisture content and water activity prediction of semi-finished cassava crackers from drying process with artificial neural network
    Article Snippet: This paper is concerned with a prediction of moisture content and water activity of semi-finished cassava crackers from a hot air drying process in a tray dryer.. A Multilayer Feedforward Neural Network in the form of Nonlinear-Auto-Regressive with Exogenous input (MFNN-NARX) was proposed to predict the product quality from the dynamic drying process.. The process was carried out at seven drying temperature settings of 50, 55, 60, 65, 70, 75, and 80 C. The MFNN-NARX was composed of one hidden layer, three exogenous inputs (drying temperature, relative humidity, and sample temperature), and two state inputs and outputs (moisture content and water activity).

    Article Title: NEURAL NETWORK PREDICTIVE CONTROL IN A NATURALLY VENTILATED AND FOG COOLED GREENHOUSE
    Article Snippet: Passive ventilation in greenhouse production systems is predominant worldwide, limiting its usability and profitability to specific regions or for short production cycles.. Evaporative fogging systems have increasingly been implemented in Arid and Semi-Arid regions to extend the production cycle during the warmest season, and also to achieve near-optimum environments for year-round production.. However, appropriate control strategies for evaporative fogging systems are still lacking or limited despite its reported benefits in terms of environmental uniformity and potential savings in water and energy usage, when compared to fan and pad systems.



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