2012 International Workshop on Nonlinear Circuits,Communications and Signal Processing
NCSP'12, Honolulu, Hawaii, USA. March 4-6, 2012

Investigation of Biometric Date Input to Multi-Layer Perceptron
Kana Kurata, Chihiro Ikuta, Yoko Uwate and Yoshifumi Nishio†Tokushima University
2–1 Minami-Josanjima, Tokushima, Japan
Phone:+81-88-656-7470
Email: {kana, ikuta, uwate, nishio}@ee.tokushima-u.ac.jp

Abstract

2. MLP with chaotic pulse wave

In this study, we propose a network model with input of biometric data to the hidden layer neurons of the Multi-LayerPerceptron (MLP). The injecting of the noise is known to beeffective to the improvement of the performance of the MLP.
We inject the biological pulse wave as a source of this noise.
We investigate the performance of the MLP when the sampled data of biological pulse waves obtained from real biological experiments are injected into the neurons. Computer
simulated results show that the performance of the biologicalpulse wave is better than the case of random noise. Further,
we investigated the characteristics of the proposed method,

The MLP is the most famous feed forward neural networkand is composed of some neuron layers. In general, the MLP
is learned by Back Propagation algorithm (BP) which wasproposed by D.E. Rumelhart [10]. This network can solve
various kinds of tasks, for example, pattern recognition, pattern classification, data mining and so on. In this study, weconsider the case that the MLP consists of three layers (wherethe number of the neurons are 2-40-1) and the hidden layer
neurons has inputs as shown in Fig. 1. The neuron has multi-1. Introduction

Neuron…

In this study, in order to investigate the effect of chaotic
oscillations of real biological signals on information processing ability, we investigate the performance of the Multi-Layer
Perceptron (MLP) with input of biometric data. The pulse
wave is a change of the volume caused by the blood current
and is measured from the surface of various parts of the body.
Biological signals including the pulse wave, the brain wave,
and the heart beat wave are said to exhibit chaotic behaviors [1][2]. We refer the biological pulse wave as the chaotic
pulse wave. Some reports suggest that healthy biological systems possess chaotic features and periodic states indicate sickor fatal conditions.
On the other hand, many algorithms injecting chaotic oscillations into the neural networks have been proposed in order
to avoid the local minimum problems [3]. We have also investigated various methods to exploit chaotic features to enhance
the ability of the neural networks [4]-[9]. However, in the paststudies, only mathematical abstract models, e.g. the logistic
map and the cubic map, are considered as chaotic source.
In this study, we inject the chaotic pulse wave as a sourceof the chaotic oscillations into the MLP. By computer simulations, we investigate the effect of the chaotic pulse wave.
We also compare the results with the cases of random noiseand sine wave.

Chaotic pulse wave

Figure 1: MLP with chaotic pulse wave.
.
input and a single output. The standard updating rule of theneuron is defined by Eq. (1).
 n
X

yi (t + 1) = f  wi j (t)x j (t) − θi (t) ,(1)j=1where y is an output of the neuron, f is an output function,w is a weight of connection, x denotes the output of otherneurons, and θ is a threshold of neuron. For this equation,
the weight of connections and the threshold of neurons arelearned by BP algorithm.

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Next, I show a proposed updating rule of the neuron. We
inject the chaotic pulse wave into the threshold of neurons.
The updating rule is described in Eq. (2) and is used to theneurons in the hidden layer.
 nXyi (t + 1) = f  wi j (t)x j (t) − θi (t) + αψi (t) ,(2)j=1where ψ is the chaotic pulse wave, α denotes the strengthof the chaotic pulse wave. In this equation, the weight ofconnections and the threshold of neurons are learned by BP
algorithm as same as the standard updating rule.
In Eqs. (1) and (2), the output function f is a sigmoidal
function described as:f (a)=1.
1 + e−a(3)
Figure 3: Example of reconstructed attractors of pulse wave.

3. Chaotic pulse wave
We have obtained pulse waves from real biological experiments. The sampling frequency of the data is 200Hz andmeasurement time is 5 minutes. The time waveform of thepulse wave and the corresponding reconstructed attractor are
shown in Fig. 2 and Fig. 3, respectively. We calculate the averaged Lyapunov exponent as 4.5032 and hence we can saythat this pulse wave is chaotic.

4.1. Two-spiral problem
We apply the proposed network for solving the Two-SpiralProblem (TSP) [11]. TSP is a problem which classifies twospirals drawn on the plane, and it is famous as a highly nonlinear problem. The MLP learns points on the two spirals byusing BP algorithm. We prepare 98 data on the two spirals
as shown in Fig. 4. The number of learning times is fixed as500000. We investigate the error which is defined as Eq. (4).

2700

1

Relative voltage

2500

0.8

2300
2100

0.6
1900

y00.4

1700
1500
11001

2001

3001

4001

10.2

5001

Time (s)0Figure 2: Example of pulse waves.

00.2

0.4

0.6

0.8

1x

In order to inject this chaotic pulse wave into the MLP, wenormalize the wave and use only one of every 7 samples.

Figure 4: Two spirals problem (target points).

4. Simulation

1 X(T n − On )2 ,N n=1
N

MS E =In this section, the difference in the performance of the proposed MLP with input of biometric data and the conventional
MLPs is investigated.
where T is the target value and O is the output value.

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We compare the results with the conventional, random
noise case, sine wave case, and the chaotic pulse wave case(proposed). The simulated results are summarized in Table 1.
From this table, the average error of the MLP with chaotic

11

0.8

0.8

0.6

0.6

y00.4

Table 1: Learning performance.
Ave.
Min.
Max.
Std. Dev.
Conv.
0.0978 0.0006 0.2518
0.0790Random
0.0717 0.0003 0.2369
0.0534
Sin0.0720 0.0085 0.2480
0.0608
Pulse wave 0.0516 0.0005 0.2452
0.0640

y1

0.2

00.4

0.2

00.2

0.4

0.6

0.8

10x(a)
Figure 6: Two spirals problem (ideal classification).

pulse wave is the smallest of all. The conventional MLP is
the worst because the conventional MLP often falls into a local minimum. Figure 5 is an example of learning curves offour MLPs. From this curve, we can confirm a good performance of the proposed MLP with chaotic pulse wave.

Table 2: Learning performance.
Ave.
Min.
Max.
Std. Dev.
Conv.
0.2030 0.0910 0.4503
0.0932
Random0.1725 0.0776 0.2924
0.0428Sin
0.1760 0.0836 0.5003
0.0895
Pulse wave 0.1600 0.0925 0.5022
0.0836

0.6
Conventional
Random
SinPulse wave

0.5
0.4
0.3

5. Conclusion

0.2

In this study, we have proposed a network model with theinjection of biometric data. We injected chaotic pulse wave
into the hidden layer neurons of the MLP. By carrying out
computer simulations, we confirmed that inputting chaoticpulse wave to MLP gains a good effect for MLP learning.

0.1
00

2000

4000

6000

8000

10000

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Figure 5: Example of learning curves.
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y0.4

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000.2

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0

1

00.2

0.4

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1xx

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0.81x(d) Proposed MLP.

Figure 7: Example of classification results.
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