Vassilis G. Kaburlasos, Gerhard X. Ritter's Computational Intelligence Based on Lattice Theory PDF

By Vassilis G. Kaburlasos, Gerhard X. Ritter

This eighteen-chapter e-book provides the newest functions of lattice idea in Computational Intelligence (CI). The booklet makes a speciality of neural computation, mathematical morphology, desktop studying, and (fuzzy) inference/logic. The booklet comes out of a different consultation held throughout the international Council for Curriculum and guideline global convention (WCCI 2006). The articles provided the following exhibit how lattice conception may well recommend attainable choices in useful clustering, type, trend research, and regression functions.

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This eighteen-chapter e-book provides the most recent functions of lattice thought in Computational Intelligence (CI). The e-book makes a speciality of neural computation, mathematical morphology, computing device studying, and (fuzzy) inference/logic. The ebook comes out of a distinct consultation held throughout the global Council for Curriculum and guideline global convention (WCCI 2006).

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2 Training based on Merging A training algorithm based on merging for a SLLP is given below. The algorithm constructs and trains an SLLP with dendritic structures to recognize the patterns belonging to the class of interest C1 . The remaining patterns in the training set are labeled as belonging to class C0 = ¬C1 . The input to the algorithm consists of a set of training patterns X = {x1 , . . X. Ritter and G. Urcid with associated desired outputs y ξ with y ξ = 1 if and only if xξ ∈ C1 , and y ξ = 0 if x ξ is not in class C1 .

Furthermore, various models of fuzzy lattice neural networks (FLNN) were studied in [5, 6] and some of their applications in the area of text classification and classification of structured data domains were presented in [7, 8]. Despite the high capabilities of LNNs, training dendritic networks such as Lattice Based Morphological Perceptrons with Dendritic Structure [13], results in creating huge neural networks with a large number of neurons; the size of the trained network sometimes is comparable to the size of the training data.

S8. ] 1 = −xξi , E = {1} and S9. ] S10. ] 1 0 < xξi and xξi < −wik } S11. ] S12. If D = ∅ then return to S2 else let D = D and loop to step S7 [Check if there is no need to wire more axonal fibers to the current dendrite. ] To illustrate the results of an implementation of the training algorithm based on elimination, we employed a data set from [21], where it was used to test a simulation of a radial basis function network (RBFN). The data set consists of two nonlinearly separable classes of 10 patterns each, where the class of interest, C1 , comprises the patterns depicted with solid circles (•).

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