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License fees and the definition of a user are making Business Intelligence systems from large vendors more and more expensive, whereas the cost of Excel is a sunk cost, having been included in Microsoft Office. Another even larger cost that is rarely discussed is the total cost for retraining and redeveloping existing business systems. Converting users to a large Business Intelligence system requires a couple of days of training at a thousand dollars or more per day. Then, when users return to work, they are expected to leave behind their existing spreadsheets and use the new system for their analysis. The reality is that it doesn t happen. People return to their desks with high hopes, fail to get the results they need (the examples in class are always dumbed down), and, under the pressure of deadlines, return to their old Excel spreadsheets. If users are forced to use a BI system, they must take time from being productive to create new solutions that replace the Excel solutions that have taken them years to create. That time away from production damages the bottom line.



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The number of partitions, however, needs to be xed by a priori expert knowledge in these approaches The nal con guration of the clusters and the corresponding fuzzy rule parameters are obtained by a nonlinear optimization However, there is no guarantee of convergence to an optimal solution, as the nal solution greatly relies on the selection of initial locations of cluster centers Also, it is dif cult and time consuming to visualize the optimal number of partitions required for modeling a complex system, particularly if the underlying structural behavior of the system is time-varying and inadequately known Under the dynamic adaptation method, the training may start with a single fuzzy rule or neuron or none at all (Er & Wu, 2002; Kasabov & Song, 2002; Rizzi et al, 2002; Wang & Lee, 2001; Wu & Er, 2000; Wu et al, 2001).





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During training, the network structure grows and concurrently the parameters of antecedents and consequents are adapted to obtain the desired modeling accuracy A constructive approach for creating on ANFIS-like network is proposed in (Mascioli & Martinelli, 1998; Mascioli et al, 2000; Rizzi et al, 2002) based on Simpson s min-max technique (Simpson, 1992;1993) The input space is partitioned by constructing hyperboxes using the min-max procedure The hyperbox-based framework facilitates application of different types of fuzzy membership functions To decrease the complexity of the network, a pruning method, called pruning adaptive resolution classi er (PARC), is developed in Rizzi et al (2002) This consists of deleting some negligible hyperboxes and a fusion procedure to make actual coverage complete This constructive approach is, however, basically developed for batch learning to create a structure-adaptive network with a signi cantly higher degree of automation.

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Figure 18.7: Rejection method for generating random variables with density fX(x) nential distributions, each with rate Xi. We therefore first generate a random integer i from the set (1, -. . , n}; this is the selection phase. We then generate a negative exponentially distributed random number with rate Xi. Cl

A dynamic fuzzy neural network (DFNN) architecture reported in Wu and Er (2000) and Er and Wu (2002) is another notably relevant work It makes use of a hierarchical learning approach and an orthogonal least-squares-based pruning technique (Chen et al, 1991) In the DFNN architecture, fuzzy rules are represented by radial basis function (RBF) neurons in the rst hidden layer However, the representation is restrictive in the sense that widths of the membership functions belonging to various inputs that create an RBF neuron of the DFNN have the same value An enhanced version of DFNN is GDFNN (Wu et al, 2001) It introduces width vectors in RBF neurons, so that the Gaussian membership functions within a neuron can be assigned appropriate widths separately The GDFNN also attempts to provide explanations for selecting the values of.

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