 Fuzzy Modeling Library (FMLib) ele241056: Estimation of the medium voltage electrical line maintenance cost in towns
DescriptionName: ele241056 Type: Realworld problem  Number of input variables: 4 Number of examples: 1056  Domain of the input variable 1: [0.5, 11] Domain of the input variable 2: [0.15, 8.55] Domain of the input variable 3: [1.64, 142.5] Domain of the input variable 4: [1, 165] Range of the output variable: [64.470001, 8546.030273] 
The problem involves to estimate the minimum maintenance costs which are based
on a model of the optimal electrical network for Spanish towns. The problem has
four input variables: sum of the lengths of all streets in the town, total area
of the town, area that is occupied by buildings, and energy supply to the town.
These values are somewhat lower than the real ones, but companies are interested
in an estimation of the minimum costs. Of course, real maintenance costs are
exactly accounted but a model that relates these costs to any characteristic
of simulated towns with the optimal installation is important for the electrical
companies. We were provided with data concerning the four characteristics of the
towns and their minimum maintenance costs in a sample of 1,056 simulated towns. In this problem, five linguistic terms are usually considered for each variable
in linguistic fuzzy modeling. Data Sets The original data set of 1,056 examples has been randomly divided into 5 different
subsets (four of them with 211 examples and one of them with 212 examples). Joining four of these subsets in a training
data set and keeping the fifth subset as test data set it is possible to build
5 different partitions of the original data set at 80%20%, i.e., a 5fold crossvalidation.
Some papers only consider a data set partition, in this case, the first partition is used. You can also download the whole data set here.The existing dependency of the four input variables with the output variable
in the first training and test data sets is shown below. (X1,Y) in ele241056.tra
 (X2,Y) in ele241056.tra
 (X3,Y) in ele241056.tra
 (X4,Y) in ele241056.tra
     (X1,Y) in ele241056.tst
 (X2,Y) in ele241056.tst
 (X3,Y) in ele241056.tst
 (X4,Y) in ele241056.tst
    
Results NonFuzzy Modeling Techniques  Method Type  Reference  Method  No. Rules  No. Labels  Training  Test  Comments  Regression  [CHS99]  Linear  17 nodes  5 par.  164,662  36,819    [CHS99]  2ndorder polynomial  77 nodes  15 par.  103,032  45,332    Neural Network  [CHS99]  3 layer perceptron 451    35 par.  86,469  33,105    Genetic AlgorithmProgramming  [San00]  GAP  50 nodes  5 par.  18,168  21,884    [San00]  Interval GAP  15 nodes  4 par.  16,263  18,325    Linguistic Fuzzy Modeling  Method Type  Reference  Method  No. Rules  No. Labels  Training  Test  Comments  Learning/tuning also the data base  [CHS99]  MOGULD  63  25  19,679  22,591    [CHV01]  Gr+MF  68  31  9,988  10,414    [CHMV01]  Gr+MF+Context  87  38  9,841  10,466    [CHMV01]  Gr+MF+Context  74  36  9,238  8,644    Extending the model structure  [CHZ01]  HSLR  97  40  22,653  23,817  Hiearchical KB  Precise Fuzzy Modeling  Method Type  Reference  Method  No. Rules  No. Labels  Training  Test  Comments  TSKtype FRBSs  [CHS99]  MOGULTSK  268  25  11,074  11,836   
ReferencesThe application was originally proposed in:
[CHS99]  O. Cordón, F. Herrera, L. Sánchez, Solving electrical distribution problems using hybrid evolutionary data analysis techniques, Applied Intelligence 10 (1999) 524.  The data has been also used in the following papers:
[San00]  L. Sánchez, Intervalvalued GAP algorithms, IEEE Transactions on Evolutionary Computation 4:1 (2000) 6472.
 [CHV01]  O. Cordón, F. Herrera, P. Villar, Generating the knowledge base of a fuzzy rulebased system by the genetic learning of the data base, IEEE Transactions on Fuzzy Systems 9:4 (2001) 667674.
 [CHMV01]  O. Cordón, F. Herrera, L. Magdalena, P. Villar, A genetic learning process for the scaling factors, granularity and contexts of the fuzzy rulebased system data base, Information Sciences 136:14 (2001) 85107.
 [CHZ01]  O. Cordón, F. Herrera, I. Zwir, Linguistic modeling by hierarchical systems of linguistic rules, IEEE Transactions on Fuzzy Systems, 2001. To appear.

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