By Teresa Serrano-Gotarredona, Bernabé Linares-Barranco, Andreas G. Andreou

ISBN-10: 144198710X

ISBN-13: 9781441987105

ISBN-10: 146134672X

ISBN-13: 9781461346722

*Adaptive Resonance conception Microchips* describes circuit options leading to effective and useful adaptive resonance conception (ART) structures. whereas artwork algorithms were constructed in software program by means of their creators, this is often the 1st ebook that addresses effective VLSI layout of paintings structures. All structures defined within the ebook were designed and fabricated (or are nearing final touch) as VLSI microchips in anticipation of the approaching proliferation of paintings functions to self sufficient clever structures. to house those structures, the publication not just presents circuit layout strategies, but additionally validates them via experimental measurements. The booklet additionally contains a bankruptcy tutorially describing 4 artwork architectures (ART1, ARTMAP, Fuzzy-ART and Fuzzy-ARTMAP) whereas offering simply comprehensible MATLAB code examples to enforce those 4 algorithms in software program. additionally, a whole bankruptcy is dedicated to different power functions for real-time information clustering and classification learning.

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**Additional resources for Adaptive Resonance Theory Microchips: Circuit Design Techniques**

**Example text**

The bottom-up set is a normalized version of the top-down set {zjt}. Therefore, from a hardware implementation point of view, it would be desirable to implement physically only a binary valued set (one bit per weight) and introduce the normalization of the bottom-up weights during the computation of terms {Tj }. This way, the two sets {ztn and {z;t} can be substituted by a single binary valued set {Zij}, and eq. 6) {ztr} N Tj =L zt'J1i = Iz~u nIl, j = I, ... 3) IThis type of modification is employed in the Fuzzy-ART model [Carpenter, 1991c].

However, moderate values of L and a are desired in practical ART1 applications. On the other hand, it can be expected that the behavior will also tend to be similar for very high values of p: if p is very close to 1, each training pattern will form an independent category. However, different training patterns will cluster into a shared category for smaller values of p. Therefore, a very similar behavior between ART1 and ART1m will be expected for high values of p, while more differences in behavior might be apparent for smaller values of p.

4. For the original ART1 L = 5, and for the ART1m a = 2. In Fig. 3, boxed category templates are those that met the vigilance criterion and had the maximum T j value. If the box is drawn with a continuous line, the corresponding Zj template suffered modifications due to learning. If the box is 4For all simulations in this Chapter, randomly generated training patterns sets were obtained with a 50 per cent probability for a pixel to be either '1' or '0'. 45 A VLSI-FRIENDLY ARTl ALGORITHM drawn with dashed line, learning did not alter the corresponding Zj template.

### Adaptive Resonance Theory Microchips: Circuit Design Techniques by Teresa Serrano-Gotarredona, Bernabé Linares-Barranco, Andreas G. Andreou

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