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A simpli ed version of the eTS learning algorithm [4] reduces the complexity of the original eTS The simpl_eTS replaces the notion of information potential with the concept of scatter and provides a similar but computationally more effective algorithm Simpl_eTS uses Cauchy membership functions, instead of Gaussians, and the notion of rules/clusters age to improve computational and modeling performance This chapter suggests a distinct approach, namely, evolving fuzzy participatory learning (ePL) to develop evolving fuzzy rule-based models The approach joins the concept of participatory learning (PL) [31] with the evolving fuzzy modeling idea [2, 3] In evolving systems the PL concept is viewed as an unsupervised clustering algorithm [28] and is a natural candidate to nd rule-base structures in evolving fuzzy modeling Here we focus on functional fuzzy (TS) models.

B. Exposure to the phobic stimulus almost invariably provokes an immediate anxiety response, which may take the form of a situationally bound or situationally predisposed panic attack. Note: in children, the anxiety may be expressed by crying, tantrums, freezing, or clinging. C. The person recognizes that the fear is excessive or unreasonable. Note: in children, this feature may be absent. 1 = absent or false

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Similar to eTS and simpl_eTS, structure identi cation and self-organization in ePL means estimation of the focal points of the rules, the antecedent parameters, except that ePL uses participatory learning fuzzy clustering instead of scattering, density, or information potential With the antecedent parameters xed, the remaining TS model parameters can be found using least-squares methods [13] Instead of using data potential as in eTS, the evolving fuzzy participatory learning captures the rule-base structure at each step using convex combinations of new data samples and the closest cluster center, the focal point of a rule Afterward, the rulebase structure is updated and, similarly to eTS and simpl_eTS, the parameters of the rule consequents are computed using the recursive least-squares algorithm.

information as ltering criteria, or simply picking the rst COS that responds). Once we have selected a COS instance, we attempt to communicate with it using Remote-Code Execution or Remote Messaging as described in Section 14.3.1.

us. In such a case, the fixed and variable costs have to be estimated in the best possible way. Suppose the Model Motel was considering renting the adjacent premises and converting the space into a 50-seat coffee shop to better serve the needs of its motel customers. The owner of the motel and the accountant have developed the cost projections shown in Exhibit 8.6. With this information we can answer the question: What must the minimum sales be to earn the return on investment desired This can be answered by using the basic CVP equation. Required sales level Fixed expenses Return on investment (profit) 100% Variable cost % $85,600 100% $115,750 45% $257,222 Assuming the estimates of costs are reasonably accurate, the owner of the Model Motel would have to decide whether the projected required sales revenue of $257,222 could be attained from motel customers and other potential customers in the area. If the volume could be reached, then the new venture would be profitable. $30,150 55%

Computational experiments with the classic Box & Jenkins benchmark are performed to compare the performance of the evolving fuzzy participatory learning (ePL) with the evolving fuzzy system modeling (eTS, simpl_eTS, xTS) and neural fuzzy modeling methods The experiments show that the ePL performs satisfactorily for this benchmark problem The ePL method is also used to forecast the average hourly load of an electricity generating plant and to compare further with eTS, xTS, neural network modeling, and neural fuzzy modeling using actual data The results con rm that evolving fuzzy participatory learning is a promising method to solve real-world problems The chapter proceeds as follows Section 42 brie y reviews the idea of evolving fuzzy rule-based modeling and the basic eTS method The next section introduces the concept of participatory learning, the features of the evolving fuzzy participatory learning (ePL) method, and its computational details Section 4.

4 considers the classic.

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