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For j m1 we prune the looser neuron as follows: c1im1 k 0 w1im1 k 0 v1im1 k 0 dm1 k 0 Then we update m1 as m1 m1 1 in order to decrease the size of the network: If dmin k > du or m1 1, do nothing Finally, we update L as L L DL Remark 82: The parameters L and DL are needed because the pruning algorithm is not active at each iteration The initial value of L is DL The pruning algorithm works at the rst time when k L; then L is increased by DL The pruning algorithm works every DL iterations The parameter DL was found empirically as 5du ; thus, the pruning algorithm has only du as the design parameter Remark 8.



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Cooked ham is also known as canned ham. The scheme for the main processing stages is shown in Figure 16.1. Main stages are the reception of hams, the brine injection, tumbling and massaging, cooking, and cooling. All these stages are described below.





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3: The clustering algorithm is somewhat similar to that given in [20 22] but they do not take the max of z1j k 1 as (819) This idea is taken from the competitive learning of ART recurrent neural network [16, 19] in order to get the winner neuron Remark 84: In [27] the second derivative of an objective function is used to nd the unimportant hidden neuron In this chapter we use the population density [3] to nd the unimportant neuron Remark 85: You can see that the structure identi cation of this recurrent network uses the informative potential of the new data sample as a trigger to update the hidden neuron (rule) base as [1 3], and it is able to satisfy the basic features of an evolving intelligent system (EIS) as in section 8.

The process of transition is similar to nucleation and growth mechanisms of incubation, induction, rapid growth, and then slow completion. For thin lms this can be explained by the Johnson Mehl Avrami equation, Zt

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the result of similar neuronal states in the brain region of interest (ROI ). In regard to alpha power, a similar state of neuronal assemblies is likely to occur during a relaxed state, resulting in summation of postsynaptic potentials (and alpha synchrony) due to similarity of neuronal states (e.g., similar to members of a chorus singing in unison). Conversely, a dissimilar state of neuronal assemblies is likely to occur during active task engagement, resulting in differential assignment of neurons and desynchrony or reduced alpha power or amplitude. The advantage of EEG is that it not only captures fastchanging events, but it can also be used to detect the timing of communication between different cortical regions by means of coherence analysis. Similarity in the spectral content of EEG recorded at different sites (i.e., high coherence) is assumed to indicate cortico-cortical communication. Only EEG and MEG can be used to definitively assess the timing or sequencing of events of hypothesized network models. A major limitation of EEG, however, is the problem of volume conduction, or the spreading of electrical charge throughout the liquid medium of the brain so that the signal

2 of this chapter; thus, it is an evolving neurofuzzy recurrent network 8:24 8:23.

sign(L(b j ))

consists of modules for the theory and for the procedure The output of the simulation is normally compared to the records of the behavior of the animals, and the parameters of the model are adjusted to maximize some measure of goodness of t Initially the analyst can adjust the parameters by trial and error, but parameters should be estimated on the basis of some automatic procedure This may be an exhaustive search of all combinations of parameter values within a certain range and step size, or it may be done by a hill-climbing search procedure Explicit solutions have been obtained for some quantitative theories These are equations that follow from the speci cations of the theory They may require some assumptions of distribution forms that are only approximately correct, and they may be restricted to a particular type of procedure.

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