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Extra info for Artificial Mind System: Kernel Memory Approach (Studies in Computational Intelligence)
For the SFS; i = 1, 2, . . , 10, corresponding to the respective class IDs, 1, 2, . . e. the ten digits). With this setting, the total number of RBFs in LTM Nets (1 to 3, for the SFS), MLT M,T otal is thus calculated as MLT M,T otal = MLT M,1 + Ncl (MLT M,2 + MLT M,3 ) which yields i) 85 for the SFS, ii) 65 for the OptDigit, and iii) 175 for the PenDigit data set, respectively. 6) were made a priori so that the STM network functions as a “buﬀer” to the LTM networks with sparsely but reasonably covering all the ten classes during the evolution.
Step 4) Create a direct path to the incoming input pattern vector for each RBF added in the previous step8 . (This data ﬂow is illustrated (bold line) in Fig. e. 17)). Note that, unlike other LTM networks, the radii values of the RBFs in LTM Net 1 must not be varied during the evolution, since the strong activation may not be actually necessary in implementation; it is considered that the input vectors to some of the RBFs within the LTM networks are simply changed from oST M to x. Then, the collection of such RBFs represents LTM Net 1.
E. e. due to the connections via the link weights in between). g. , 2002). In addition, the duration of which such state variables within the two kernel units are so set and held can, however, be varied, during the later learning process by the AMS. e. e. e. g. the speciﬁc car had some mechanical fault and caused a traﬃc accident in the past. e. e. due to the memory recall during the interactive data processing amongst the associated modules) and eventually exhibit a fear response due to the functionality of the emotion module.
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