小故障
发表于 2025-3-21 16:52:10
书目名称Computational Intelligence Techniques for Bioprocess Modelling, Supervision and Control影响因子(影响力)<br> http://impactfactor.cn/2024/if/?ISSN=BK0232405<br><br> <br><br>书目名称Computational Intelligence Techniques for Bioprocess Modelling, Supervision and Control影响因子(影响力)学科排名<br> http://impactfactor.cn/2024/ifr/?ISSN=BK0232405<br><br> <br><br>书目名称Computational Intelligence Techniques for Bioprocess Modelling, Supervision and Control网络公开度<br> http://impactfactor.cn/2024/at/?ISSN=BK0232405<br><br> <br><br>书目名称Computational Intelligence Techniques for Bioprocess Modelling, Supervision and Control网络公开度学科排名<br> http://impactfactor.cn/2024/atr/?ISSN=BK0232405<br><br> <br><br>书目名称Computational Intelligence Techniques for Bioprocess Modelling, Supervision and Control被引频次<br> http://impactfactor.cn/2024/tc/?ISSN=BK0232405<br><br> <br><br>书目名称Computational Intelligence Techniques for Bioprocess Modelling, Supervision and Control被引频次学科排名<br> http://impactfactor.cn/2024/tcr/?ISSN=BK0232405<br><br> <br><br>书目名称Computational Intelligence Techniques for Bioprocess Modelling, Supervision and Control年度引用<br> http://impactfactor.cn/2024/ii/?ISSN=BK0232405<br><br> <br><br>书目名称Computational Intelligence Techniques for Bioprocess Modelling, Supervision and Control年度引用学科排名<br> http://impactfactor.cn/2024/iir/?ISSN=BK0232405<br><br> <br><br>书目名称Computational Intelligence Techniques for Bioprocess Modelling, Supervision and Control读者反馈<br> http://impactfactor.cn/2024/5y/?ISSN=BK0232405<br><br> <br><br>书目名称Computational Intelligence Techniques for Bioprocess Modelling, Supervision and Control读者反馈学科排名<br> http://impactfactor.cn/2024/5yr/?ISSN=BK0232405<br><br> <br><br>
放牧
发表于 2025-3-21 23:22:39
978-3-642-42486-1Springer-Verlag Berlin Heidelberg 2009
率直
发表于 2025-3-22 03:25:29
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Lethargic
发表于 2025-3-22 08:38:44
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松果
发表于 2025-3-22 11:26:11
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没有准备
发表于 2025-3-22 13:20:43
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没有准备
发表于 2025-3-22 19:08:28
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Inertia
发表于 2025-3-22 22:49:08
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thwart
发表于 2025-3-23 05:22:29
Guolin Ma,Shufan Wen,Yun Huang,Yubin Zhoue first part of the chapter a theoretical framework for estimation of general process kinetic rates based on Artificial Neural Network (ANN) models is introduced. Two scenarios are considered: i) Partly known (measured) process states and completely known kinetic parameters; ii) Partly known process
使痛苦
发表于 2025-3-23 09:30:24
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