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Titlebook: Artificial Neural Networks for Intelligent Manufacturing; Cihan H. Dagli (Associate Professor) Book 1994 Springer Science+Business Media D

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期刊全稱Artificial Neural Networks for Intelligent Manufacturing
影響因子2023Cihan H. Dagli (Associate Professor)
視頻videohttp://file.papertrans.cn/163/162673/162673.mp4
學(xué)科分類Intelligent Manufactoring Series
圖書封面Titlebook: Artificial Neural Networks for Intelligent Manufacturing;  Cihan H. Dagli (Associate Professor) Book 1994 Springer Science+Business Media D
影響因子The quest for building systems that can function automatically has attracted a lot of attention over the centuries and created continuous research activities. As users of these systems we have never been satisfied, and demand more from the artifacts that are designed and manufactured. The current trend is to build autonomous systems that can adapt to changes in their environment. While there is a lot to be done before we reach this point, it is not possible to separate manufacturing systems from this trend. The desire to achieve fully automated manufacturing systems is here to stay. Manufacturing systems of the twenty-first century will demand more flexibility in product design, process planning, scheduling and process control. This may well be achieved through integrated software and hardware archi- tectures that generate current decisions based on information collected from manufacturing systems environment, and execute these decisions by converting them into signals transferred through communication network. Manufacturing technology has not yet reached this state. However, the urge for achieving this goal is transferred into the term ‘Intelligent Systems‘ that we started to use
Pindex Book 1994
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Hybrid intelligent systems: Tools for decision making in intelligent manufacturing
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Artificial Neural Networks for Intelligent Manufacturing
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Artificial Neural Networks for Intelligent Manufacturing978-94-011-0713-6
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Advances in Solid State Physicsesentations are derived and the performance under each is analyzed. The results indicate that input representation is particularly important in determining the neural network’s recognition capability and the amount of tolerable noise in parameter readings.
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Intelligent Manufactoring Serieshttp://image.papertrans.cn/b/image/162673.jpg
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Die Beugung an periodischen Strukturen,The complex demands of many modern applications cry out for flexible, adaptable devices that can quickly react to changing circumstances as solutions rather than mechanical brute force. Artificial neural systems (ANS) promise this capability. It is the intent of this chapter to realize ways to evaluate this accomplishment and its performance.
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