Download Adaptive Business Intelligence by Zbigniew Michalewicz PDF

By Zbigniew Michalewicz

Adaptive company intelligence structures mix prediction and optimization concepts to aid choice makers in advanced, quickly altering environments. those structures deal with primary questions: what's more likely to ensue sooner or later? what's the top plan of action? Adaptive enterprise Intelligence explores components of information mining, predictive modeling, forecasting, optimization, and flexibility. The booklet explains the appliance of diverse prediction and optimization thoughts, and indicates how those strategies can be utilized to boost adaptive platforms. insurance comprises linear regression, time-series forecasting, choice timber and tables, man made neural networks, genetic programming, fuzzy structures, genetic algorithms, simulated annealing, tabu seek, ant structures, and agent-based modeling.

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6, we discussed how prediction, optimization, and adaptability were used in the “pollution control” research project in Poland. The prediction module was responsible for predicting the ecological damage that each 30 km × 30 km square would sustain during the next 48 hours; the optimization module (consisting of an evolutionary algorithm) was responsible for recommending the ideal energy production level for each power station; and the adaptability module was responsible for tuning the system’s performance.

Many of those cars may still be in transit, and if they are going to the same auction site, then they might be sold on the same day. x Inventory. It is important to monitor the inventory level of cars at all auction sites, as each site has a particular throughput. If an auction can handle 250 cars per sale day, and the current inventory is larger than 250, then additional time must be added to the estimated sale date. x Dynamic market conditions. Market prices for cars change quite frequently, sometimes slowly and sometimes very quickly.

In other words, the adaptability module can “adapt” to environmental changes by updating these rules, with the update frequency depending on how fast the environment changes. Of course, a really good adaptability module can make its own decision on the update frequency by continuously measuring its own prediction errors. Hence, a really good adaptability module can adapt its own speed of adaptation! It is important to note, however, that the adaptability module’s effectiveness in updating the prediction module is influenced by the type of prediction methods that were used to build the underlying models.

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