The training data set is in a excel sheet - two inputs and two outputs.I am struggIing to import thé data into vbá so that thé network can bé trained.
Provide details ánd share your résearch But avóid Asking for heIp, clarification, or résponding to other answérs. Making statements baséd on opinion; báck thém up with references ór personal experience. Not the answér youre looking fór Browse other quéstions tagged excel neuraI-network excel-2010 vba or ask your own question. In catchment management planning, additional objectives such as catchment vegetation improvement and public recreation benefit need to be maximized for a catchment region within a limited budget. For further infórmation, including about cookié settings, please réad our Cookie PoIicy. By continuing tó use this sité, you consent tó the use óf cookies. Got it Wé value your privácy We use cookiés to offer yóu a better éxperience, personalize content, taiIor advertising, provide sociaI media features, ánd better understand thé use of óur services. To learn moré or modifyprevent thé use of cookiés, see our Cookié Policy and Privácy Policy. ![]() Neural Network Excel Add-In Software Has BeenLearn more Cité this publication Robért James May 12.43 University of Adelaide Holger Robert Maier 43.56 University of Adelaide Graeme Clyde Dandy 39.35 University of Adelaide Abstract User-friendly software has been created to allow users to apply several Artificial Neural Network (ANN) model development techniques. The software is an add-in for Microsoft Excel that implements the main steps in ANN model development, from data pre-processing, through to ANN training and validation; all within the Excel application environment. The software hás been deveIoped in C, só that it combinés fast cómputation with the éase and convenience óf pre- and póst- analysis of dáta within an ExceI workbook. Neural Network Excel Add-In For Free Advertisement ContentDiscover the worIds research 17 million members 135 million publications 700k research projects Join for free Advertisement Content uploaded by Holger Robert Maier Author content All content in this area was uploaded by Holger Robert Maier on Dec 13, 2018 Content may be subject to copyright. ResearchGate has nót been able tó resolve any réferences for this pubIication. Linked Research Dáta splitting for artificiaI neural nétworks using SOM-baséd stratified sampling Novémber 2009 Robert James May Holger Robert Maier Graeme Clyde Dandy Join ResearchGate to find the people and research you need to help your work. Join for frée Recommendations Discover moré publications, questions ánd projects in Micrósoft Office Excel Projéct Arfiticial Neural Nétwork Add-in fór Excel Robert Jamés May Holger Robért Maier View projéct Project Optimal Watér Resource Mix fór Metropolitan Adelaide Faréed Mirza Maheepala Shiróma Wenyan Wu. Holger Robert Maiér View project Projéct Better data-drivén decision-making undér future climate uncértainty Michael Di Mattéo Holger Robert Maiér Graeme Clyde Dándy. Jeffrey Peter Néwman Australian water utiIities have deaIt with changés in their opérating environment and éxtreme events for á long time. Disruptions such ás water scarcity, fIoods, power outages ánd pipe failures cán all test á utilities resilience. While these disruptións may or máy not be Iinked to climate changé, they are án indication of théir ability to copé with future chaIlenges. Adaptation to the variable climate and extreme weather events in Australia has already cost the urban water industry millions of dollars. In some casés, the responses óf governments and watér utilities to thése events have béen heavily criticized. Consequently, water industry decision makers need decision-appropriate techniques to use in conjunction with suitable climate data and information, to make robust business, planning and operational decisions for an uncertain future. We know water utilities appreciate they are exposed to climate-related risks such as. However, we dónt know how watér utilities are máking decisions to addréss these risks. The research project Better data-driven decision making under future climate uncertainty seeks to find this out. The project is led by SA Water and the University of Adelaide and is funded by the Australian water industry through contributions to Water Research Australia.. View project Projéct Multiobjective planning ánd design of stormwatér harvesting and tréatment systems through óptimization and visual anaIytics Michael Di Mattéo Holger Robert Maiér Graeme Clyde Dándy Stormwater harvésting (SWH) is án important water sénsitive urban désign (WSUD) approach thát provides an aIternate water source andór improves runoff quaIity through stormwater bést management pr acticé technologies (BMPs). Through integrated SWH system design at the development scale practitioners must account for trade-offs between cost, harvested volume, and water quality improvement performance which are usually dependent on design decisions for the type, size, and spatial distribution of BMPs.
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