Adaptive Resonance Theory Microchips: Circuit Design by Teresa Serrano-Gotarredona, Bernabé Linares-Barranco,

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

Adaptive Resonance idea Microchips describes circuit innovations leading to effective and useful adaptive resonance conception (ART) platforms. whereas artwork algorithms were built in software program through their creators, this can be the 1st booklet that addresses effective VLSI layout of paintings platforms. All platforms defined within the e-book were designed and fabricated (or are nearing of entirety) as VLSI microchips in anticipation of the upcoming proliferation of paintings functions to self sufficient clever structures. to house those structures, the e-book not just presents circuit layout concepts, but in addition validates them via experimental measurements. The ebook additionally encompasses a bankruptcy tutorially describing 4 paintings architectures (ART1, ARTMAP, Fuzzy-ART and Fuzzy-ARTMAP) whereas delivering simply comprehensible MATLAB code examples to enforce those 4 algorithms in software program. moreover, a complete bankruptcy is dedicated to different strength functions for real-time facts clustering and class studying.

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7 is simplified to the one shown in Fig. 8. Note that now there is no Match- Tracking and no weights update (and, of course, no weights initialization). The ARTl module vigilance parameter Pa is set to the initial value Pa used during training. Under these circumstances the ARTMAP output is class vector yb which is made equal to vector W J, ADAPTIVE RESONANCE THEORY ALGORITHMS 19 Initialize P,,: Po , = P" Read Input Pattern: a=(a" """' aN) , , ART! N T j =~>V'li ;=1 Winner-Takes-All: y;=l if T rmnxj ( ~) y/,=Oij NJ ..

85, while for lower values of Pa the performance is degraded. 85 what is happening is that the training exemplars tend to create new categories in each training epoch which they tend to abandon in the next epoch (because it became too degraded after clustering together too many exemplars). e. they are not accessed by any of the training exemplars. ADAPTIVE RESONANCE THEORY ALGORITHMS .. • 35 .. 0 .. 17. 001. The training set are the spiral points and the test set is the 100 x 100 grid points 36 ADAPTIVE RESONANCE THEORY MICROCHIPS Comparison to Back-Propagation Lang and Wit brock [Lang, 1989} tried to train a Back Propagation network to learn to tell these two spirals apart.

1(b), an extra division operation T j = LII n zjl/(L - 1 + IZjl) needs to be performed for each node in layer F2. This is an expensive hardware operation and would probably constitute a performance bottleneck in the overall system for both analog and digital circuit implementations. If possible, it would be very desirable to avoid this division operation. 2. Illustration of the simplification process of the division operation: (a) original division operation, (b) piece-wise linear approximation, (c) linear approximation Fig.

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