Artificial Intelligence & Data Mining Applications in the by Shahab D. Mohaghegh (Ed.), Saud M. Al-Fattah (Ed.), Andrei

By Shahab D. Mohaghegh (Ed.), Saud M. Al-Fattah (Ed.), Andrei S. Popa (Ed.)

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4-Schematic of the recognition system showing output layer arrangements. SPE Formation Evaluation, September 1993 235 Derivative Plot Modell Model 2 Model 3 Model 4 Model 5 Model 15 Model 16 The interpretation model suggested by the system is a prelimi, nary model and should be verified with other information sources, such as other diagnostic plots, geologic information, well log data, and history matching, as Ref. 12 discusses. The history-matching results indicate that the parameters obtained from analyzing the infinite-acting model accurately describe the actual pressure history of the well (Fig.

1. 11. , and the PDP Research Group: Parallel Distributed Processing: Exploration in the Microstructure of Cognition-Psychological and Biological Models, The MIT Press, Cambridge, MA (1986) Chap. 2. 12. : "Vsing An Expert System To Identify the Well-Test Interpretation Model," 1PT (May 1990) 654. 7 Then we explain the stepby-step learning procedure. We use Fig. I to clarify the derivation procedure. Backpropagation Learning Rule. , links between Layers i and k). , links between Layers i and j).

2) frequently is used as a threshold function and has the form OJ = I/[J+exp( - I j )], .............................. (1) where OJ = the output from a node in the j layer (Fig. I) and I j =the sum of the input signals to this node: Ij = E Wji 0i' . . . . . . . . . . . . . . . . . . . (2) i This summarizes the calculation that occurs in a single node. Learning is the most important computationally and memoryintensive task in a neural net operation. In a mathematical sense, learning is the process by which we can find a set of weights that produces the expected output when a net is presented within an input.

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