Applying Computational Intelligence [electronic resource] : How to Create Value / by Arthur Kordon.

By: Kordon, Arthur [author.]Contributor(s): SpringerLink (Online service)Material type: TextTextLanguage: English Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2010Description: XXII, 459p. 20 illus. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783540699132Subject(s): Engineering | Data mining | Artificial intelligence | Engineering design | Technology | Engineering | Computational Intelligence | Technology Management | Data Mining and Knowledge Discovery | Artificial Intelligence (incl. Robotics) | Engineering DesignAdditional physical formats: Printed edition:: No titleDDC classification: 006.3 LOC classification: Q342Online resources: Click here to access online
Contents:
Computational Intelligence in a Nutshell -- Artificial vs. Computational Intelligence -- A Roadmap Through the Computational Intelligence Maze -- Let's Get Fuzzy -- Machine Learning: The Ghost in the Learning Machine -- Evolutionary Computation: The Profitable Gene -- Swarm Intelligence: The Benefits of Swarms -- Intelligent Agents: The Computer Intelligence Agency (CIA) -- Computational Intelligence Creates Value -- Why We Need Intelligent Solutions -- Competitive Advantages of Computational Intelligence -- Issues in Applying Computational Intelligence -- Computational Intelligence Application Strategy -- Integrate and Conquer -- How to Apply Computational Intelligence -- Computational Intelligence Marketing -- Industrial Applications of Computational Intelligence -- The Future of Computational Intelligence -- Future Directions of Applied Computational Intelligence.
In: Springer eBooksSummary: In theory, there is no difference between theory and practice. But, in practice, there is. Jan L. A. van de Snepscheut The ?ow of academic ideas in the area of computational intelligence has penetrated industry with tremendous speed and persistence. Thousands of applications have proved the practical potential of fuzzy logic, neural networks, evolutionary com- tation, swarm intelligence, and intelligent agents even before their theoretical foundation is completely understood. And the popularity is rising. Some software vendors have pronounced the new machine learning gold rush to “Transfer Data into Gold”. New buzzwords like “data mining”, “genetic algorithms”, and “swarm optimization” have enriched the top executives’ vocabulary to make them look more “visionary” for the 21st century. The phrase “fuzzy math” became political jargon after being used by US President George W. Bush in one of the election debates in the campaign in 2000. Even process operators are discussing the perf- mance of neural networks with the same passion as the performance of the Dallas Cowboys. However, for most of the engineers and scientists introducing computational intelligence technologies into practice, looking at the growing number of new approaches, and understanding their theoretical principles and potential for value creation becomes a more and more dif?cult task.
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Computational Intelligence in a Nutshell -- Artificial vs. Computational Intelligence -- A Roadmap Through the Computational Intelligence Maze -- Let's Get Fuzzy -- Machine Learning: The Ghost in the Learning Machine -- Evolutionary Computation: The Profitable Gene -- Swarm Intelligence: The Benefits of Swarms -- Intelligent Agents: The Computer Intelligence Agency (CIA) -- Computational Intelligence Creates Value -- Why We Need Intelligent Solutions -- Competitive Advantages of Computational Intelligence -- Issues in Applying Computational Intelligence -- Computational Intelligence Application Strategy -- Integrate and Conquer -- How to Apply Computational Intelligence -- Computational Intelligence Marketing -- Industrial Applications of Computational Intelligence -- The Future of Computational Intelligence -- Future Directions of Applied Computational Intelligence.

In theory, there is no difference between theory and practice. But, in practice, there is. Jan L. A. van de Snepscheut The ?ow of academic ideas in the area of computational intelligence has penetrated industry with tremendous speed and persistence. Thousands of applications have proved the practical potential of fuzzy logic, neural networks, evolutionary com- tation, swarm intelligence, and intelligent agents even before their theoretical foundation is completely understood. And the popularity is rising. Some software vendors have pronounced the new machine learning gold rush to “Transfer Data into Gold”. New buzzwords like “data mining”, “genetic algorithms”, and “swarm optimization” have enriched the top executives’ vocabulary to make them look more “visionary” for the 21st century. The phrase “fuzzy math” became political jargon after being used by US President George W. Bush in one of the election debates in the campaign in 2000. Even process operators are discussing the perf- mance of neural networks with the same passion as the performance of the Dallas Cowboys. However, for most of the engineers and scientists introducing computational intelligence technologies into practice, looking at the growing number of new approaches, and understanding their theoretical principles and potential for value creation becomes a more and more dif?cult task.

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