平成18年度電気関係学会四国支部連合大会

323

Behavior of Fatigable SOM
with Fatigue Level

17 - 27

Masato TOMITA(Tokushima University)

Haruna MATSUSHITA(Tokushima University)

Yoshifumi NISHIO(Tokushima University)

1. Introduction

4. Simulation results

The Self-Organizing Map (SOM) is popular algorithm
for unsupervised learning and visualization introduced by
Teuvo Kohonen. One of the most attractive applications ofSOM is clustering and several algorithms for various kindsof clustering problems have been reported and investigated.
In this study, we propose a new type of SOM algorithm,
which is called Fatigable SOM (FSOM) algorithm. The
important feature of FSOM is that the neurons of FSOM
are fatigable. We investigate the behavior of FSOM andapply FSOM to clustering problems. Further, we introduce the fatigue level to FSOM to increase its flexibility
for various kinds of clustering problems. The efficienciesof FSOM and the fatigue level are confirmed by several
simulation results.

Input data is 2-dimensional random data of 800 points
whose distribution is non-uniform as Fig. 1(a). We consider
the conventional SOM and the proposed FSOM with 100
neurons (10 × 10). From the results, we can say that theconventional SOM tends to self-organize all the input data
including a lot of noises and FSOM self-organizes some ofthe clusters. On the other hand, FSOM with the fatigue
level self-organizes only one cluster and is not influenced
by noises.

2. Fatigable SOM (FSOM)

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In the FSOM algorithm, the neurons which have become a winner can not become a winner during a certainperiod of time. We call the neuron which has become awinner the fatigued neuron. We have very little difficultyin understanding why neurons are fatigued with becoming
a winner. Because human-beings are fatigued with doing
something, neurons also are fatigued.
(FSOM1) The initial values of all the weight vectors are
given between 0 and 1 at random.
(FSOM2) An input data is inputted to all the neurons at
the same time in parallel.
(FSOM3) We find the winner neuron among all theneurons except the neurons which have become winners(namely, the fatigued neurons) by calculating the distance
between the input data and the weight vector of the neuron.
(FSOM4) The weight vectors of all the neurons are updated.
(FSOM5) The winner neuron is labeled as a fatigued neuron.
(FSOM6) If FSOM has nf fatigued neurons, all the fatigued neurons are recovered and turn to be normal neurons.
(FSOM7) The steps from (FSOM2) to (FSOM6) are repeated for all the input data.

3. FSOM with Fatigue Level
We introduce a fatigue level to FSOM in order to increase the flexibility of FSOM. The basic algorithm is thesame as the FSOM learning algorithm and we add an additional index controlling update of the weight vectors.
We assign a variable pi indicating the fatigue level toeach neuron of FSOM. This fatigue level pi is updated
according to the following rule. We set the initial valueof pi as 3.0. If a neuron becomes a winner, pi decreases
0.5. Further, pi of all the neurons decrease according to
the update of the weight vector. The value of pi is reset to
3.0 when the fatigued neurons are recovered by (FSOM6).
The fatigue level influences the update of the weightvectors. This rule is added to realize the movement suchthat the neurons with lower fatigue levels move slower.

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Figure 1: Learning results. (a) Input data. (b) The conventional SOM. (c) FSOM. (d) FSOM with the fatigue
level.

5. Conclusions
In this study, we have proposed the Fatigable SOM(FSOM). We have explained the differences between SOMand FSOM and FSOM with the fatigue level with learningalgorithm and have investigated its behavior. Furthermore,
we have applied the proposed FSOM to extract only one ofsome clusters including a lot of noises and have confirmed
its efficiency.
In the future, we try to discover new applications of
FSOM in diverse fields such as sound data processing.

References[1] T. Kohonen, “The Self-Organizing Maps,” Neurocomputing, vol. 21, pp. 1-6, 1998.
[2] T. Kohonen, Self-Organizing Maps, Berlin, Springer,
vol. 30, 1995.
[3] H. Matsushita and Y. Nishio, “Competing Behavior ofTwo Kinds of SOMs and its Application to Clustering,”
Proc. of WSOM’05, pp. 355-362, 2005.
[4] M. Tomita and H. Matsushita and Y. Nishio “Data Extraction by Self-Organizing Map Containing Neurons
with Additional States,” RISP Journal of Signal Processing, vol. 10, no. 4, Jul. 2006 (printing).