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I (Xi ; Y |Xi 1 , Xi 2 , . . . , X1 ).
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Developing a computational model, called an evolving fuzzy neural recurrent network, that meets these seven requirements is one of the objectives of the current research. In this chapter an online evolving neuro-fuzzy recurrent network (ENFRN) was proposed to address these problems. Structure and parameters learning are active each time step of the algorithm. It generates a new hidden neuron if the smallest distance between the new data and all the existing hidden neurons (the winner neuron) is more than a given threshold, as in [20 22]. When a new hidden neuron is generated, the center of the membership function of the antecedent part takes the state of the nonlinear system and the center of the consequent part takes the output of the nonlinear system, as in [1], [3], and [25]. A new pruning algorithm is proposed based on the number of data associated to each hidden neuron with its population density. The hidden neuron that has the smallest population density (the looser neuron) is pruned if the value of its population density is smaller than a prespeci ed threshold. In order to have faster parameter convergence, a modi ed least-squares algorithm is used to train the centers and the widths in the hidden layer and the centers in the output layer in parameter learning, as in [2] and [24]. The stability of the proposed algorithm was proven and the bound of the average identi cation error was found theoretically.
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14.3.1.1 Remote-Code Execution Remote-Code Execution is the rst generalpurpose Remoteability mechanism that we will consider. It allows one COS instance (the caller) to call functions, procedures, routines, or methods that are implemented in another COS instance (the callee) even when the caller and callee are deployed on different machines. Usually, we want to try to make this remote execution call look as similar as possible to a local execution call in the programming language. Depending on the particular implementation mechanism chosen for Remote-Code Execution, there will be varying levels of support for heterogeneity across the components. Some implementations are programming language speci c but support multiple operating systems and hardware con gurations. Other implementations can support multiple programming languages but only work on a single operating system or a single hardware con guration. Obviously, it is not likely that a single approach will cover all possible programming languages, hardware con gurations, and operating systems seamlessly. Remote-Code Execution alternatives can be implemented using either a non-object-oriented approach or an object-oriented approach. In the next few paragraphs we will describe the non-object-oriented approach and the object-oriented approach. Then we will compare these alternatives. Because there are many issues common to both alternatives, we will consider how values are passed between the Local and Remote COSs. Similarly, we will consider how the control ow in the calling program works for a multitasking caller and callee. Lastly, we will consider how errors and exceptions are handled in both Remote-Code Execution alternatives.
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where is the covariance matrix of a set of unbiased estimators for the parameters and J 1 ( ) in the sense that the difference J 1 is a nonnegative de nite matrix. We will not go into the details of the proof for multiple parameters; the basic ideas are similar. Is there a relationship between the Fisher information J ( ) and quantities such as entropy de ned earlier Note that Fisher information is de ned with respect to a family of parametric distributions, unlike entropy, which is de ned for all distributions. But we can parametrize any distribution f (x) by a location parameter and de ne Fisher information with respect to the family of densities f (x ) under translation. We explore the relationship in greater detail in Section 17.8, where we show that while entropy is related to the volume of the typical set, the Fisher information is related to the surface area of the typical set. Further relationships of Fisher information to relative entropy are developed in the problems.
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There are approximately 150,000 members of the National Association of Social Workers (NASW, 2003). Social workers have typically
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In existing rate adaptation schemes such as ARF [7] and the RBAR [10], the bene t of using high data rates has not been fully explored. Since control
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The numerator of Equation 25.13 represents the sum of the order agreement between corresponding pairs of scores. If the order of the pair is the same on both variables, then (dihx )(dihy ) = +1. If the order is not the same, (dihx )(dihy ) = 1. Finally, if one or both pairs are tied, (dihx )(dihy ) = 0. There are a number of forms of Kendall s tau that can be de ned by altering the denominator of Equation 25.13. When there are no ties on X or Y, then the sum of squared dominance scores is equal to the number of pairs for the (i > h) comparisons, which is (1/2) n (n 1). Therefore, tau may be expressed in the form Kendall (1970) calls tau-a, dihx dihy ta =
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