"JOURNAL OF RADIOELECTRONICS" N 3, 2002 |

METHODOLOGY OF INDISTINCT IDENTIFICATION FOR IDENTIFICATION OF ELECTRONIC TRAFFIC CONTROL SYSTEMS

O.Svegzda ^{1) }
, e-mail:
ly1df@one.lt,

V.Bagdonas^{1) }
, e-mail:
valtek@eaf.ktu.lt** **

T. Magyla ^{1,2)}
, e-mail:
Tomas.Magyla@eaf.ktu.lt

^{
1)}Kaunas
Technology University KTU, Kaunas, Lithuania

^{2)}Railways
Infrastructure Modernization Group,

**Lithuanian Railways
LG PIU, Kaunas, Lithuania**

** **

Received on March 20, 2002.

** **

*
This article contains development of an indistinct (fuzzy) methodology for
identification of electronic systems, identification criteria form and a
sample application of proposed methodology for evaluation of implementation
impact of a centralized electronic traffic control system in LG. The proposed
methodology performs solution of identification problem by the help of
indistinct sets and expert evaluations. Therefore the intellect of an expert
is treated as an instrument of measurement, which is used for evaluation of
identification criteria. The
identification of criteria in proposed methodology is performed in a trapezoid
form of a membership function. Created methodology enables to evaluate
influence of implementation of one or another electronic system and
characterizes how good one or another decision alternative is suitable for
evaluation needs, according to the formulated criteria of evaluation. The
developed methodology was used for evaluation and analysis of implementation
influence of a centralized traffic control system in LG, as dependence set of
technical, social and other factors. *

**1.**
Introduction

**3.**
The Methodology of Evaluation

**4.**
results

**5.**
CONCLUSIONS

**6.**
References

Increasing complexity in electronic system hierarchy has caused a huge interest in using knowledge-based “fuzzy” identification. “Fuzzy” identification is based on new approaches to generation and selection of fuzzy logic families [1, 2, 3, 4, 5, 6]. The role of inference with uncertainty is becoming more and more important in real-time electronic processes. The efforts for building fuzzy decision-making systems to assist decision in non-standard situations have been widely discussed [7, 8, 9]. Different types of expert systems have been used for this or similar purposes, based on different approaches to fuzzy logics data acquisition. The corner stone of the fuzzy inference mechanism is fuzzy algorithm [10, 11] which consists from the following procedures: conversion of the input identification indistinct data from the research object (process, action) to the universal scale with rated values; processing of the universal scale rated input values by using fuzzy rule system to the output values of universal rated scale; conversion of the output rated values to the normal dimension output signals for inference or formation of the control output signal. Almost the same idea is used in expert systems [12, 13, 14, 15, 16] where experts perform initial evaluation of the parameter by evaluating that parameter in fuzzy numbers with different form of a membership function (the process of “fuzzification”); on the next phase, the data array is processed by applying fuzzy rules; and on the end the received fuzzy expression is “defuzzificated” thus obtaining alternatives for inference or the final result of identification or evaluation.

So far no comprehensive approach
exists for evaluating and finding objective *overall impact *of
implementation of a new railway electronics projects, among the most
complicated decision/evaluation problems that exist. In complicated railway
electronics, decision-making situations where research objects are huge
electronic traffic interlocking or centralized traffic control systems,
evaluation and optimization, based on classical methods, is impossible, as the
only one acceptable way for identification of evaluation facets (from our
point of view) is by using the abovementioned fuzzy algorithm idea adopted for
expert inference mechanism. In most of fuzzy expert systems the standard
identification and inference methods [12, 13,
17] are used. Processing of expert submitted evaluation data can be based
on a fuzzy analytic hierarchy pair-wise comparison method [2,
13, 15], weight coefficient space transformation method
[18], or other robust methods of analysis [19,
20]; some of direct calculation methods are based on the fuzzy inference
software packages (FuzzyCalc, Mathlab Fuzzy workbench, Fagoal). No one of
these standard methods have enough strength to withstand the inadequateness,
caused by the non-linearity of the expert utility function, as in most of
these methods expert initial evaluation results are taken for granted. We have
proved many times, that inadequateness, caused by the shortage of expert
experience or non-linearity of expert utility function can mutilate both
intermediate results of inference process, and the final result of
identification or evaluation [21, 22, 23]. Therefore the
modified methodology, free from the abovementioned shortcomings has been
proposed in this article. We have proposed to evaluate the utility of
implementation of electronic centralized traffic control system by using
expert evaluations in indistinct numbers, in a form of a trapezoid membership
function, as we are confident about advantages, obtained by using this
evaluation method. The methodology of evaluation and weighting of criteria in
a trapezoid membership form is proposed in this article, together with the
results of a sample evaluation in railways electronics.

** **

Fig. 1

Environmental issues of railways electronics has long been considered as positive and environmental-friendly, therefore no effective measures have been taken to prevent waste growth, however, there is an increasing recognition, that many processes used to produce electronic systems do have environmental consequences (as a result of materials used, power consumed, or end-of life product consideration); some electronic products do have increasing disposal problems [17, 27, 28]. The replacement of old relay-based circuits with newly developed electronic computerized traffic control systems will result in a huge amount of electronics waste, designated for landfill. An effective, decision-based recycling is one option that may aid in reducing the volume of electronics waste designated for landfill disposal, with the possible decision model, which can be motivated on using expert evaluations.

The other unwanted effect of implementation of centralized electronic traffic control systems- job loses, that are related to the declining human supervision demand in new electronic traffic control systems. As experience shows, electronics and computer technology boosts with an exponential acceleration, and a new Artificial Intellect mindset will be the engine for inference. Human supervision demand in the wide area Centralized Traffic Control Systems [29] is declining and needed only for occasional system maintenance procedures, but not for daily influence. That results job loses and in turn, job loses result in many unwanted societal sideband effects.

The abovementioned problems are quite important in railway electronics. Proper measures can compensate or even eliminate unwanted effects of system level decisions, if applied to a planned situation. The situation can be planned, by referring to a prognosis of evaluation. Our aim in this article is to propose a methodology that is capable to adequately identify the relationships between attributes and objectives of both technical and institutional issues of electronic traffic control systems in railways.

In simple electronic traffic control systems simulation of the electronic network throughput capacity could be an efficient way out for evaluating the impact of the given system [22]. It is quite informative, especially for a single layer hierarchy system. But in multi-attribute multi-layer electronic traffic control systems complexity overruns informative issues. Therefore the ordinary way for performing multi-criteria evaluation of the research object (electronic traffic control systems) is by using group expert evaluations, submitted in expressions with indistinct numbers, characterized by “triangle” [6, 22] type membership function. Every facet from the developed decision problem hierarchy is evaluated separately by providing it with corresponding membership form. Fig 2 represents a well-known example of indistinct number in a form of triangular membership.

*Fig. 2*

If we analyze it in more details, we’ll notice, that the triangular membership function (see Fig 2.) is derived from expert evaluation information, and can be formalized as follows:

1.
submitted probability
*p, *
which is usually in a form of interval _{
},
where the value of the evaluated hierarchy criterion *A* may be (see
Fig 3, a detail view of a triangle membership);

2.
typical and most expected fuzzy value *x*
of evaluated criterion, by the opinion of an expert.

In that
case the evaluation of criterion *A* can be formulated like this:

Fig. 3

This form is quite desirable for the knowledge engineer, but psychologically is inconvenient for an expert, performing the evaluation: it may be hard to interpret separate parts of the membership function. Due to this and some other reasons (some of these reasons are quite personal characteristics of the expert), statistics is usually used to characterize the perfection of an expert [15, 30, 31]. But in some new and un-traditional decision problems statistics data may not exist. Our experiments show [6, 21, 23], that evaluations, performed by a single expert, may fluctuate a lot, depending on expert experience, and may not be time-constant, therefore such methodology of evaluation is imprecise and does not worth to pay much attention. Therefore we propose a weight coefficient (kind of expert “calibration” procedure), which is engaged to characterize expert’s ability to evaluate precisely. Moreover, we suggest engage a group evaluation:

where*
_{
}*-
is a direct evaluation of evaluation facet

where*
_{
}*

But from psychological point of view a more convenient way (see Fig 4) is to use indistinct evaluations in trapezoid membership. In that case an expert is asked to point out:

- an
interval
_{ }, where the most typical value of the criterion*A*is, by the opinion of an expert (see Fig 5); -
subjective probability
*p*(which is in a form of an interval_{ }) under which the expert is right.

In that
case the evaluation of the indication *A** *is as follows:

Fig. 4

Then a group expert evaluation in a trapezoid membership is as follows:

Fig. 5

A trapezoid
type membership function expression _{
} from
equation (5) can be formulated as follows (see Fig 5):

where

We can easily notice, that formula (7) is valid only if:

For the
ease of calculations, we’ll make an assumption, that _{
}*;*
or else the membership formula (7) has to be modified.

Modification 1): if _{
};
then

Modification 2): if _{
};
then

Modification 3): if *b>2x _{1}*; then

where

_{
}

Modification 4): if *b>2(1-x _{2})*;
then

where

_{
}.

As we can clearly see from Fig 3 and Fig 5,
evaluation in a trapezoid membership function posses the same functionality,
like evaluations in a triangle membership function, if only* _{
}*and

Let us assume, that expert evaluations are of a real-life,
and inadequateness, which is dependent upon the non-linearity of expert’s
subjective efficiency function [23] is present together with
other expert shortages. Non-linearity of expert’s subjective efficiency
function is characteristic for non-typical decisions, projects, reforms, where
expert conservatism takes place; also in making decisions where an expert is a
concerned person. Trying to diminish or eliminate the mistakes derived from
non-linearity of the expert’s subjective efficiency function, a correction
procedure is needed: both the expert’s submitted direct evaluation of
evaluation facet *A* _{
} and
a Parett set of partial evaluation facets _{
} have
to be enlarged by the number _{
},
equal to the *j*-th expert risk (evaluating object, process or action *
A*) supplement function, to become “complete”- _{
}.
The “incompleteness” elimination procedure for submitted
evaluations* *_{
} and
_{
},
can be formulated as follows [23]:

Actually, the expert risk supplement function is calculated only after
obtaining the expert’s efficiency function. This function depends on the
evaluated factor *A *and can be time dependant. Due to this reason
efficiency function has to be measured at the time of performing expert
interviewing. The procedure of efficiency function measurement is quite
complicated, therefore it is purposeful to measure it at single point and only
then (with acceptable error) the expert’s turn for non-risk can be considered
as constant: _{
}.
The expert’s _{
}can
also be calculated by using classical lottery methods [23].
In general case a weight coefficient for summing up expert’s efficiency
function must be included into the complete rated indistinct expression for a
group evaluation (formula (4)), and in that case it must be
as follows:

where:

_{
}-
direct evaluation of evaluation object *A *_{ }by the *j-*th
expert;

* j = _{
}* -
index of the expert (the index of the goal in
multi-attribute evaluations is

* _{
}-
*complex expert weight coefficient. For the
triangular membership case it can be formulated as following:

where

* _{
}*is
a weight coefficient, characterizing expert’s experience level (“ability to
evaluate in chime’);

_{
} is
a weight coefficient, characterizing expert’s distinctness of evaluations;

_{
} is
a weight coefficient, characterizing expert’s efficiency function (its
expression was discussed earlier).

For the
triangle membership case, the best illustration of *
_{
}* is
from geometrical point of view- it is a magnitude of intersection point

The bigger
is the area, the more distinct is expert evaluation.
The same idea can be used for the trapezoid
membership case (see Fig 5). From geometrical point of
view, * _{
}*is
a magnitude of an intersection point

The bigger is the area of intersection, the more distinct is expert evaluation. For the trapezoid case, we would like to propose to calculate weight coefficient, characterizing expert’s experience level by applying fuzzy integral (so called “fuzzy” expected value”) expression [32, 33]:

where:

_{
}-
fuzzy integral [32] of _{
}by
indistinct measure *x*(.) (“_{}”
means “minimum”);

_{} and
_{
} -indistinct
evaluations, characterized by their membership functions, correspondingly _{
} and
_{
};
where _{
}represents
(trapezoid _{
})
direct evaluation of *A*, and _{
} represents
(trapezoid * _{
}*)
summarized (Parett) evaluation of

Weight
coefficient _{
},
characterizing distinctness of expert’s evaluations, (for a trapezoid case) is
formulated by applying fuzzy integral expression and by using the same
conception as for the triangular membership case:

Finally, a
coefficient is needed (lets us call it _{
})
to eliminate the transformation “triangle to trapezoid” influence. This
influence is depending only on the trapezoid top length *a*, namely on
the interval_{}.
If we split this interval into two equal parts _{
}and
_{
},
the central point *x* can be treated like a median value of the triangle,
the transformation triangle-to-trapezoid influence instantly becomes reduced
twice. Coefficient _{
}is
as follows:

where _{
} is
a minimal average length of trapezoid top _{
}from
submitted expert evaluations;*
_{
}*.
Then the complex weight coefficient for a trapezoid membership function is as
follows:

Then the
complex indistinct evaluation of an object, process or action *A *with a
trapezoid membership function is characterized by:

As a result of our proposed
methodology, an expression of a complex indistinct evaluation of
implementation expediency for centralized electronic traffic control system,
where subsequent hierarchy facets are characterized* *with a trapezoid
membership function, was obtained (formula (23)). Formula (23)
was experimentally tested and obtained results were compared with
experimentally (by the help of formula (15)) obtained
results of evaluation in triangular membership, and results of mathematical
simulation by applying GPSS [22]. The comparison has proved
precision of calculations and low indistinctness of prognosis by using (23)
formula. Comparison procedure was executed by applying developed formulas to a
small electronic centralized traffic control network between two nodes, as
GPSS is not suitable for more complex solutions.

The second phase of experiments included results, obtained after the evaluation of a centralized electronic traffic control system (CTC) EbiScreen. As an example of attribute hierarchy for EbiScreen, attributes of electronic interlocking Ebilock 950, experimentally examined earlier [13], were chosen. For the evaluation of CTC EbiScreen we decided on three main branches of evaluation attributes: technical, environmental and social attribute branches (Fig 1). The process of evaluation of electronic CTC EbiScreen has included evaluation of the overall impact of CTC implementation as a sum impact value of the main three attribute branches versus the utility of the existing old CTC system. Expert submitted evaluations have been processed by applying formula (23). The branch of technical attributes have been scaled down to a lower hierarchy facets: implementation issues, system costs, system performance, technical benefits and technology interaction for the end user, system maintainability, maintenance cost and ease, technical support, administrative issues, future expansion prospects and other facets, scaled down to a lower branch of hierarchy (see Fig 1). The branch of environmental attributes: amount of waste for landfill (slag, ash, non-recyclable materials), recycling process impacts (global warming, acidification, photochemical ozone creation, possible photo-smog), recovery rate and cost of the recycled materials as a function of recycling scenario (Ag, Al, Cu, Fe, Hg, Pb and others). Recovery rate is mostly dependent on the amount of recovered metals and on the cost of recycling [28]. Different scenarios of end-of-life electronics recycling have been analyzed, during the evaluation of environmental issue. Due to the limitations of this article scope the evaluation results have been reviewed quite shortly, and the main environmental dependency impacts can be seen from the picture (see Fig 6).

Fig. 6

*Fig. 7*

The most undefined issue of evaluation is the branch of social impacts, depending on centralized electronic traffic control system implementation. It was evaluated as: possible unemployment rate (gross national product, societal behavior issues). A separate factor hierarchy as well as methodology can be developed for this evaluation; but in this research it was analyzed as a single factor with negative weight. Finally, the overall value of implementation of a new CTC system is illustrated in Fig 7, as a compromise between technical benefits of implemented electronic system and environmental factor together with social issues.

1. In more complicated decision-making situations, where the object of research is a complicated hierarchy electronic system, standard methods of situational planning and simulation are powerless to evaluate overall value of implementation expediency. The overall impact or overall value of a centralized electronic traffic control system implementation in this research is understood as composition value of evaluation facets or optimal alternative versus the existing system and other alternatives, by the opinion of experts, performing evaluation.

2. Widely used expert evaluation triangle membership function can be easily transformed into evaluation with normal trapezoid membership fuzzy expression, so that the median value usage and trapezoid top splitting permits to reduce the transformation error twice.

3. A trapezoid membership function offers benefits in statistics, because of the likeness of the function shape, related to the Normal distribution.

2. Inadequateness of evaluations is originated from the influence of expert personal interest. It can be compensated by applying methods of experiment planning. The expert efficiency function is time dependant, and due to this reason it has to be measured at the time of performing expert interviewing.

3. Inadequateness of evaluations, caused by the problem of an external inconsistence, can be compensated, by applying ranking to the experts, performing evaluations. The additional ranking means forming complex weight coefficients for evaluation of each expert.

4. This research has shown that implementation of a new electronic CTC system does not always assure 100 percent perfect solution of all the problems; on the contrary, it may develop new environmental or social problems. Only a good planning and analysis together with evaluation of alternatives may help.

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