![Page 1: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/1.jpg)
1/56
Family of Family of SSelf-elf-OOrganized rganized NNetwork Inspired etwork Inspired by by IImmune mmune AAlgorithm (lgorithm (SONIASONIA) and ) and
Their Various ApplicationsTheir Various Applications
Dept. of Computational Intelligence & Systems ScienceTokyo Institute of Technology
Muhammad R. Widyanto03D35190
博士本審査 博士本審査 2006.01.042006.01.04
SONIA
F-SONIA
SONIA-DNN CMF-SONIA
EF-SONIA
![Page 2: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/2.jpg)
2/56
Thesis Road MapThesis Road MapChapter 1Chapter 1
IntroductionIntroduction
Chapter 2 Chapter 2 [J1][J1]SONIA and Food Quality PredictionSONIA and Food Quality Prediction
[J[Jxx]: Journal Paper ]: Journal Paper xx-th-th
Chapter 3 Chapter 3 [J2][J2]SONIA-DNN and Preference ModelingSONIA-DNN and Preference Modeling
Chapter 4 Chapter 4 [J3][J3]F-SONIA and Fragrance RecognitionF-SONIA and Fragrance Recognition
Chapter 5 Chapter 5 [J4][J4]CMF-SONIA and Overlapping Pat. Clas.CMF-SONIA and Overlapping Pat. Clas.
Chapter 6 Chapter 6 [J5][J5]EF-SONIA and Unknown Odor Recog.EF-SONIA and Unknown Odor Recog.
Chapter 7Chapter 7ConclusionsConclusions
SONIA
F-SONIASONIA-DNN CMF-SONIA
EF-SONIA
![Page 3: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/3.jpg)
3/56
ContentsContentsSONIA
F-SONIA
SONIA-DNN CMF-SONIA
EF-SONIA
Chap. 3 SONIA-DNN for Preference ModelingChap. 3 SONIA-DNN for Preference Modeling
Chap. 4 F-SONIA for Fragrance RecognitionChap. 4 F-SONIA for Fragrance Recognition
Chap. 5 CMF-SONIA for Overlapping Pattern Class.Chap. 5 CMF-SONIA for Overlapping Pattern Class.
Chap. 6 EF-SONIA for Unknown Odor RecognitionChap. 6 EF-SONIA for Unknown Odor Recognition
Chap. 7 ConclusionsChap. 7 Conclusions
Chap. 2 SONIA and Food Quality PredictionChap. 2 SONIA and Food Quality Prediction
Chap. 1 IntroductionChap. 1 Introduction
![Page 4: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/4.jpg)
4/56
Problems Problems Chap. 1 IntroductionChap. 1 Introduction
Global ResponseGlobal Response
OverfittingOverfitting
Low GeneralizationLow Generalization
BPNN BPNN [Rumelhart, 86][Rumelhart, 86]Back-Propagation Neural NetworkBack-Propagation Neural Network
![Page 5: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/5.jpg)
5/56
OpportunityOpportunityChap. 1 IntroductionChap. 1 Introduction
Immune Algorithm Immune Algorithm [Timmis, 01][Timmis, 01]
Local ResponseLocal Response
Characteristics only Characteristics only
Diverse RepresentationDiverse Representation
![Page 6: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/6.jpg)
6/56
Chap. 1 IntroductionChap. 1 Introduction
SolutionSolution
BPNN BPNN [Rumelhart,86][Rumelhart,86] Immune AlgorithmImmune Algorithm [Timmis,01][Timmis,01]
Better RecognitionBetter Recognition
Better Generalization Better Generalization
A A SSelf-elf-OOrganized rganized NNetwork etwork inspired by inspired by IImmune mmune AAlgorithmlgorithm[proposed][proposed]
SONIASONIA
![Page 7: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/7.jpg)
7/56
Chap. 1 IntroductionChap. 1 IntroductionApplicationsApplications
SONIASONIA
Food Quality PredictionFood Quality Prediction
Preference ModelingPreference Modeling
Fragrance RecognitionFragrance Recognition
Unknwon Odor Recog.Unknwon Odor Recog.
Overlapping Pat. Clas.Overlapping Pat. Clas.
SONIA
F-SONIA
SONIA-DNN CMF-SONIA
EF-SONIA
![Page 8: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/8.jpg)
8/56
Chap. 1 IntroductionChap. 1 Introduction
ContentsContents
Chap. 2 SONIA and Food Quality PredictionChap. 2 SONIA and Food Quality Prediction
Chap. 3 SONIA-DNN for Preference ModelingChap. 3 SONIA-DNN for Preference Modeling
Chap. 4 F-SONIA for Fragrance RecognitionChap. 4 F-SONIA for Fragrance Recognition
Chap. 5 CMF-SONIA for Overlapping Pattern Class.Chap. 5 CMF-SONIA for Overlapping Pattern Class.
Chap. 6 EF-SONIA for Unknown Odor RecognitionChap. 6 EF-SONIA for Unknown Odor Recognition
Chap. 7 ConclusionsChap. 7 Conclusions
SONIA
![Page 9: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/9.jpg)
9/56
・・・
・・・
・・・
Input layer
Hiddenlayer
Outputlayer
BPNN :BPNN :[Rumelhart,86]
Input Vector Hidden Unit
Antigen
Immune Immune Algorithm :Algorithm :[Timmis,01]
Recognition Ball (RB)
SSelf-elf-OOrganized rganized NNetwork inspired by etwork inspired by IImmune mmune AAlgorithmlgorithm
[proposed]
Chap. 2 SONIAChap. 2 SONIA
![Page 10: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/10.jpg)
10/56
Input Vector
Unit Centroid
Hidden UnitHidden UnitRecognition Ball (RB)Recognition Ball (RB)
B CellAntibody
Antigen
Paratope
Epitope EuclidianDistance
Chap. 2 SONIAChap. 2 SONIA
Recognition Ball & Hidden Unit Recognition Ball & Hidden Unit [proposed]
![Page 11: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/11.jpg)
11/56
Antibody GenerationAntibody Generation [Timmis,01][Timmis,01]
Antigen[1..m]
Input Vector [1..m]
Hidden Unit CreationHidden Unit Creationof BPNN of BPNN [proposed][proposed]
Hidden Unit 1
Hidden Unit 2
Hidden Unit i
MutatedHidden Unit n
RB 2
RB i
Mutated RB n
RB 1
B-Cell Construction & Mutation B-Cell Construction & Mutation Chap. 2 SONIAChap. 2 SONIA
[proposed]
![Page 12: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/12.jpg)
12/56
Training Data
Approximation
Chap. 2 SONIAChap. 2 SONIABPNN BPNN [Rumelhart, 86][Rumelhart, 86]
Approximation Error : 0.01994
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
x
h(x)
BPNN Regularization BPNN Regularization [MacKay, 92][MacKay, 92]
Approximation Error : 0.00241
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
x
h(x)
SONIA without mutation SONIA without mutation Approximation Error : 0.01008
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
x
h(x)
SONIA with mutation SONIA with mutation [proposed][proposed]
Approximation Error : 0.00118
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
x
h(x)
![Page 13: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/13.jpg)
13/56
Supermarket
Food StoreMarket Area
Production Area
Frozen Truck
Perishable Food
Quality Control Server
Prediction Engine:Neural Networks
Quality Quality CheckCheck
Chap. 2 SONIAChap. 2 SONIA
Food Quality PredictionFood Quality Prediction
Collaborative Project with Japan Ministry of Agriculture and CSD Inc.
![Page 14: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/14.jpg)
14/56
Data Collection :Data Lodger
Data Collection :Data Lodger
011016_t
- 5051015202530
1 23
45
67
89
111
133
155
177
199
Series1Series2
Channel 1
Channel 2
Time-temperature Data
Time
oC( X 5 Minutes )
Feature Extraction :Mean & Standard Deviation
Feature Extraction :Mean & Standard Deviation
Range Selected
A B C D E
Neural Networks
ch1:Mean
ch1:SD
ch2:Mean
ch2:SD
Quality
good
Pre-Processing :Range Selection
Pre-Processing :Range Selection
Chap. 2 SONIAChap. 2 SONIA
Prediction SystemPrediction System [proposed]
Collaborative Project with Japan Ministry of Agriculture and CSD Inc.
![Page 15: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/15.jpg)
15/56
Chap. 2 SONIAChap. 2 SONIARecognition AccuracyRecognition AccuracyCollaborative Project with Japan Ministry of Agriculture and CSD Inc.
TOP
Recognition (%)
100
50
0MIDDLE BOTTOM
TOP
MIDDLE
BOTTOM
SONIA
BPNN
![Page 16: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/16.jpg)
16/56
Chap. 1 IntroductionChap. 1 Introduction
ContentsContents
Chap. 2 SONIA and Food Quality PredictionChap. 2 SONIA and Food Quality Prediction
Chap. 3 SONIA-DNN for Preference ModelingChap. 3 SONIA-DNN for Preference Modeling
Chap. 4 F-SONIA for Fragrance RecognitionChap. 4 F-SONIA for Fragrance Recognition
Chap. 5 CMF-SONIA for Overlapping Pattern Class.Chap. 5 CMF-SONIA for Overlapping Pattern Class.
Chap. 6 EF-SONIA for Unknown Odor RecognitionChap. 6 EF-SONIA for Unknown Odor Recognition
Chap. 7 ConclusionsChap. 7 Conclusions
SONIA-DNN
![Page 17: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/17.jpg)
17/56
DDecision ecision MMaker (aker (DMDM) Preference) Preference
Price: 5 million yenPrice: 5 million yen
Engine: 3000 ccEngine: 3000 cc
Consumption: 10km/lConsumption: 10km/l
PreferencePreferenceValueValue
DDecision ecision MMaker (aker (DMDM))
Alternative1:Alternative1: Nissan FugaNissan Fuga
Modeling Modeling DM Preference ???DM Preference ???
Chap. 3 SONIA-DNNChap. 3 SONIA-DNN
JSPS Center Of Excellence Project
![Page 18: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/18.jpg)
18/56
Preference Value by ComparisonsPreference Value by Comparisons
Alternative1Alternative1:: Nissan FugaNissan Fuga
Alternative2:Alternative2: Toyota Mark XToyota Mark X
ComparisonComparisonValueValue
DDecision ecision MMaker (aker (DMDM))
Chap. 3 SONIA-DNNChap. 3 SONIA-DNN
![Page 19: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/19.jpg)
19/56
ComparisonComparisonValueValue
SONIA(1)
SONIA(2)
SONIASONIA-based -based DDecision ecision NNeural eural NNetwork etwork [proposed]
Alternative 1
Alternative 2
Chap. 3 SONIA-DNNChap. 3 SONIA-DNN
JSPS Center Of Excellence Project
Incomplete Comparisons
Better Generalization
![Page 20: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/20.jpg)
20/56
Alter-native 1
Alter-native 2
Alter-native 3
・ ・ ・
Alter-native n
Alter-native 1
ー 1.2 0.8 1.3 0.9
Alter-native 2
ー ー 1.1
Alter-native 3
ー ー ー 0.7・・・
ー ー ー ー
Alter-native n
ー ー ー ー ーDDecision ecision
MMaker (aker (DMDM)) Limited Training DataLimited Training Data
Too many!
Incomplete ComparisonsIncomplete ComparisonsChap. 3 SONIA-DNNChap. 3 SONIA-DNN
JSPS Center Of Excellence Project
![Page 21: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/21.jpg)
21/56
Lp-metric Function BenchmarkLp-metric Function Benchmark [Sun, 1996]
AlternativeAlternativePreferencePreference
ValueValue
DDecision ecision MMaker (aker (DMDM))
1/
*
1
( )pI
pi i i
i
V L z z
*
: Preference Value, : Maximum Value
: Number of Criteria, : Weight Parameter
: Max Vector Value, : Alternative Vector
: Number of Dimension
V L
I
z z
p
Chap. 3 SONIA-DNNChap. 3 SONIA-DNN
![Page 22: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/22.jpg)
22/56
Experimental SettingExperimental Setting
Alternative Vector
1 2 3 4 5 6 7
1 ー
2 ー ー
3 ー ー ー
4 ー ー ー ー
5 ー ー ー ー ー
6 ー ー ー ー ー ー
7 ー ー ー ー ー ー ー
21 comparison values21 comparison values
7 discarded randomly, 14 training samples7 discarded randomly, 14 training samples
Chap. 3 SONIA-DNNChap. 3 SONIA-DNN
![Page 23: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/23.jpg)
23/56
BPNN-DNN SONIA-DNN
Average Error (%)
4
2
0
Experimental ResultExperimental Result
Excellent!
Chap. 3 SONIA-DNNChap. 3 SONIA-DNN
![Page 24: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/24.jpg)
24/56
ExperimentsExperiments
AverageError (%)
Number of Samples
0
8
4
1218 15
BPNN-DNN[Chen, 2004]
SONIA-DNN[proposed]
Chap. 3 SONIA-DNNChap. 3 SONIA-DNN
JSPS Center Of Excellence Project
Wonderful!
![Page 25: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/25.jpg)
25/56
Chap. 1 IntroductionChap. 1 Introduction
ContentsContents
Chap. 2 SONIA and Food Quality PredictionChap. 2 SONIA and Food Quality Prediction
Chap. 3 SONIA-DNN for Preference ModelingChap. 3 SONIA-DNN for Preference Modeling
Chap. 4 F-SONIA for Fragrance RecognitionChap. 4 F-SONIA for Fragrance Recognition
Chap. 5 CMF-SONIA for Overlapping Pattern Class.Chap. 5 CMF-SONIA for Overlapping Pattern Class.
Chap. 6 EF-SONIA for Unknown Odor RecognitionChap. 6 EF-SONIA for Unknown Odor Recognition
Chap. 7 ConclusionsChap. 7 Conclusions
F-SONIA
![Page 26: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/26.jpg)
26/56
Perfume IndustryPerfume IndustryHuman ExpertsHuman Experts
Artificial OdorArtificial OdorDiscrimination SystemDiscrimination System
Pure PerfumePure Perfume
Two MixtureTwo Mixture
Three MixtureThree Mixture
ProblemProblemComplexityComplexity
Chap. 4 F-SONIAChap. 4 F-SONIA
Odor Discrimination SystemOdor Discrimination System
![Page 27: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/27.jpg)
27/56
Sensory System
Frequency Counter System
Neural Network
Artificial Odor Discrimination SystemArtificial Odor Discrimination SystemChap. 4 F-SONIAChap. 4 F-SONIA
Collaboration with University of IndonesiaUnder Indonesia Ministry of Sciences & Technology Project
![Page 28: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/28.jpg)
28/56
SONIA : Hidden UnitSONIA : Hidden Unit
Input Vector
Unit Centroid
F-SONIA : Fuzzy Hidden UnitF-SONIA : Fuzzy Hidden Unit
Fuzzy Input Vector
Fuzzy Unit Centroid
EuclideanDistance
FuzzySimilarity
Chap. 4 F-SONIAChap. 4 F-SONIA
Collaboration with University of IndonesiaUnder Indonesia Ministry of Sciences & Technology Project
FFuzzy Similarity based uzzy Similarity based SONIASONIA (1/4) (1/4) [proposed][proposed]
![Page 29: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/29.jpg)
29/56
SONIA :SONIA : Crisp Value
minimum mean maximumF-SONIA :F-SONIA :[proposed][proposed]
1
Frequency
MembershipValue
Fuzzy Triangular Number
Chap. 4 F-SONIAChap. 4 F-SONIA
Collaboration with University of IndonesiaUnder Indonesia Ministry of Sciences & Technology Project
FFuzzy Similarity based uzzy Similarity based SONIASONIA (2/4) (2/4) [proposed][proposed]
![Page 30: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/30.jpg)
30/56
Similarity Value (μ)
1
MembershipValue
Frequency
Input Vector Hidden Unit Vector
Chap. 4 F-SONIAChap. 4 F-SONIA
Collaboration with University of Indonesia Under Indonesia Ministry of Sciences & Technology Project
FFuzzy Similarity based uzzy Similarity based SONIASONIA (3/4) (3/4) [proposed][proposed]
![Page 31: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/31.jpg)
31/56
SONIASONIA : :
F-SONIA :F-SONIA : [proposed][proposed]
Square Root of Quadratic Distances
Arithmetic Mean ofSimilarity Measures
Hidden Unit Input Unit
Sensor 1
Sensor i
・・・
Sensor 1
Sensor i
・・・
FFuzzy Similarity based uzzy Similarity based SONIASONIA (4/4) (4/4) [proposed][proposed]
Chap. 4 F-SONIAChap. 4 F-SONIA
Collaboration with University of IndonesiaUnder Indonesia Ministry of Sciences & Technology Project
Euclidean Euclidean DistanceDistance
Fuzzy Fuzzy SimilaritySimilarity
![Page 32: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/32.jpg)
32/56
Chap. 4 F-SONIAChap. 4 F-SONIA
Citrus-Canangga-Ethanol(%)Citrus-Canangga-Ethanol(%)Collaboration with University of IndonesiaUnder Indonesia Ministry of Sciences & Technology Project
F-SONIA[proposed]
Recognition (%)
100
50
0 SONIA FLVQ[Sakuraba,91]
LVQ[Kohonen,86]
BPNN[Rumelhart,86]
![Page 33: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/33.jpg)
33/56
0 100 200 300 400 500 600 700 800 900 10000
0.02
0.04
0.06
0.08
0.1
0.12
SONIA
F-SONIA
Error
Epoch
Chap. 4 F-SONIAChap. 4 F-SONIA
Error ConvergenceError Convergence
Collaboration with University of IndonesiaUnder Indonesia Ministry of Sciences & Technology Project
![Page 34: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/34.jpg)
34/56
1 22 1/ 2
1 21 1 1
2 1/ 2 2
1
2 1/ 2
1
( , ) ( )
( )
( )
(
)
(
I
I
I
M M N
SONIA ai aia b i
N
bi aii
N
ai bii
g
g
g
D x x
x x
x x
2 1/ 2 2
1
( ) )IN
bi bii
g x x
SONIASONIA F-SONIAF-SONIA
1 2
22
11
1 21 1
22
21
,( , )
,
I
I
N
ai ai aiM Mi
F SONIAa b I
N
bi bi bii
I
x xD
N
x x
N
1 2 1 2( , ) ( , )SONIA F SONIAD D
Chap. 4 F-SONIAChap. 4 F-SONIADissimilarity ComparisonDissimilarity ComparisonCollaboration with University of IndonesiaUnder Indonesia Ministry of Sciences & Technology Project
1 2 2
1 21 1 1
( , )HM M N
aj bja b j
D x x
Dissimilarity Definition[Hastie,01]
![Page 35: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/35.jpg)
35/56
Chap. 1 IntroductionChap. 1 Introduction
ContentsContents
Chap. 2 SONIA and Food Quality PredictionChap. 2 SONIA and Food Quality Prediction
Chap. 3 SONIA-DNN for Preference ModelingChap. 3 SONIA-DNN for Preference Modeling
Chap. 4 F-SONIA for Fragrance RecognitionChap. 4 F-SONIA for Fragrance Recognition
Chap. 5 CMF-SONIA for Overlapping Pattern Class.Chap. 5 CMF-SONIA for Overlapping Pattern Class.
Chap. 6 EF-SONIA for Unknown Odor RecognitionChap. 6 EF-SONIA for Unknown Odor Recognition
Chap. 7 ConclusionsChap. 7 Conclusions
CMF-SONIA
![Page 36: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/36.jpg)
36/56
Errors in Classification
Adaptive Clustering inspired by Adaptive Clustering inspired by B-Cell Construction of SONIA B-Cell Construction of SONIA
Class A
Class B
Chap. 5 CMF-SONIAChap. 5 CMF-SONIA
Overlapping DataOverlapping Data
![Page 37: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/37.jpg)
37/56
Chap. 5 CMF-SONIAChap. 5 CMF-SONIA
CClass lass MMajority ajority F-SONIAF-SONIA [proposed][proposed]
Class Majority for each ClusterClass Majority for each Cluster
Reduce Errors in Classification
Class A
Class B
Good Idea!
![Page 38: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/38.jpg)
38/56
Chap. 5 CMF-SONIAChap. 5 CMF-SONIA
Vowel Data Vowel Data [Lippmann,89][Lippmann,89]
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
hidhead
had
hod
heed
who’d
hawed
hudheardhood
0 750 F1(Hz)
F2(Hz)
2000
0
![Page 39: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/39.jpg)
39/56
CMF-SONIA[proposed]
Recognition (%)
80
40
0 F-SONIA BPNN[Rumelhart,86]
Excellent!
Chap. 5 CMF-SONIAChap. 5 CMF-SONIA
Recognition AccuracyRecognition Accuracy
![Page 40: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/40.jpg)
40/56
0 750 F1(Hz)
F2(Hz)
2000
0
Chap. 5 CMF-SONIAChap. 5 CMF-SONIA
Classification PlaneClassification Plane
Wow!
![Page 41: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/41.jpg)
41/56
Chap. 1 IntroductionChap. 1 Introduction
ContentsContents
Chap. 2 SONIA and Food Quality PredictionChap. 2 SONIA and Food Quality Prediction
Chap. 3 SONIA-DNN for Preference ModelingChap. 3 SONIA-DNN for Preference Modeling
Chap. 4 F-SONIA for Fragrance RecognitionChap. 4 F-SONIA for Fragrance Recognition
Chap. 5 CMF-SONIA for Overlapping Pattern Class.Chap. 5 CMF-SONIA for Overlapping Pattern Class.
Chap. 6 EF-SONIA for Unknown Odor RecognitionChap. 6 EF-SONIA for Unknown Odor Recognition
Chap. 7 ConclusionsChap. 7 Conclusions
EF-SONIA
![Page 42: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/42.jpg)
42/56
Chap. 6Chap. 6 EF-SONIAEF-SONIAUnknown Odor RecognitionUnknown Odor Recognition
Collaboration with University of IndonesiaUnder Indonesia Ministry of Sciences & Technology Project
Input
Neural NetsNeural Nets
Known Odor Unknown Odor
![Page 43: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/43.jpg)
43/56
Far with High SimilarityFar with High Similarity
High SimilarityHigh Similarity No Similarity
Arithmetic Mean
Chap. 6Chap. 6 EF-SONIAEF-SONIA
![Page 44: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/44.jpg)
44/56
Euclidean Fuzzy Similarity Euclidean Fuzzy Similarity [proposed][proposed]
NoSimilarity
Chap. 6Chap. 6 EF-SONIAEF-SONIA
![Page 45: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/45.jpg)
45/56
Similarity MeasureSimilarity Measure
Similarity Value (μ)
1
MembershipValue
Euclidean Dimension
Input Vector Hidden Unit Vector
??
??
Chap. 6Chap. 6 EF-SONIAEF-SONIA
![Page 46: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/46.jpg)
46/56
Fuzziness RegionFuzziness Region
First Dimension
Sec
ond
Dim
ensi
on
??
??
Averaging ApproachAveraging Approach
Elliptical ApproachElliptical Approach
Chap. 6Chap. 6 EF-SONIAEF-SONIA
![Page 47: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/47.jpg)
47/56
Elliptical Approach Elliptical Approach [proposed][proposed]
Θ
Brilliant Idea!
Chap. 6Chap. 6 EF-SONIAEF-SONIA
![Page 48: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/48.jpg)
48/56
Citrus-Canangga-Ethanol(%)Citrus-Canangga-Ethanol(%)
Excellent!
Method Unknown
Category Only (%)
Overall Recognition
(%)
EF-SONIA with Elliptical Approach [proposed]
96.17 98.33
EF-SONIA with Averaging Approach
89.47 96.67
Fuzzy Learning Vector Quantization (FLVQ)
[Sakuraba,91]
73.32 76.66
Learning Vector Quantization (LVQ)
[Kohonen,86]
57.63 37.91
Chap. 6Chap. 6 EF-SONIAEF-SONIA
![Page 49: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/49.jpg)
49/56
Chap. 1 IntroductionChap. 1 Introduction
ContentsContents
Chap. 2 SONIA and Food Quality PredictionChap. 2 SONIA and Food Quality Prediction
Chap. 3 SONIA-DNN for Preference ModelingChap. 3 SONIA-DNN for Preference Modeling
Chap. 4 F-SONIA for Fragrance RecognitionChap. 4 F-SONIA for Fragrance Recognition
Chap. 6 EF-SONIA for Unknown Odor RecognitionChap. 6 EF-SONIA for Unknown Odor Recognition
Chap. 7 ConclusionsChap. 7 Conclusions
SONIA
F-SONIA
SONIA-DNN CMF-SONIA
EF-SONIA
Chap. 5 CMF-SONIA for Overlapping Pattern Class.Chap. 5 CMF-SONIA for Overlapping Pattern Class.
![Page 50: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/50.jpg)
50/56
Chap. 7 ConclusionsChap. 7 ConclusionsResearch ResultsResearch Results
SONIA FamilySONIA Family- Proposed Methods -- Proposed Methods -
SONIASONIA
SONIA-DNNSONIA-DNN
F-SONIAF-SONIA
CMF-SONIACMF-SONIA
- Applications -- Applications -
Food Quality PredictionFood Quality Prediction
Preference ModelingPreference Modeling
Fragrance RecognitionFragrance Recognition
Overlapping Patt. Class.Overlapping Patt. Class.
SONIA
F-SONIA
SONIA-DNN CMF-SONIA
EF-SONIA
EF-SONIAEF-SONIA Unknown Odor Recog.Unknown Odor Recog.
![Page 51: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/51.jpg)
51/56
SONIA
F-SONIA
SONIA-DNN CMF-SONIA
EF-SONIA
- Educational Institutes -- Educational Institutes - - Industrial Companies -- Industrial Companies -
- Governments -- Governments -
Chap. 7 ConclusionsChap. 7 ConclusionsResearch ImpactsResearch Impacts
Univ. of IndonesiaUniv. of Indonesia
Tokyo Inst. of Tech.Tokyo Inst. of Tech.
CSD Inc.CSD Inc.
IURIIURI
Japan Ministry of AgricultureJapan Ministry of Agriculture
Indonesia Ministry of Sciences & Tech.Indonesia Ministry of Sciences & Tech.
Japan Society for the Promotion of ScienceJapan Society for the Promotion of Science
![Page 52: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/52.jpg)
52/56
Related Publications Related Publications (1/5)(1/5)
Journal PapersJournal PapersM. R. Widyanto et al., “Improving Recognition and Generalization Capability of "Back-Propagation NN using a Self-Organized Network inspired by Immune Algorithm”, Applied Soft Computing Journal, Elsevier Science Pub., Vol. 6, No. 1, 2005.
[J1][J1]
M. R. Widyanto et al., “SONIA based Decision Neural Networks for Preference Assessment with Incomplete Comparisons”, International Journal of Advanced Computational Intelligence & Intelligent Informatics, Vol. 9, No. 6, 2005.
[J2][J2]
M. R. Widyanto et al., “A Fuzzy Similarity based Self-Organized Network Inspired by Immune Algorithm for Three Mixture Fragrances Recognition”, IEEE Transactions on Industrial Electronics, Vol.53, No.1, 2006 (to appear).
[J3][J3]
M. R. Widyanto et al., “Class Majority in Designing Fuzzy Local Approximation NN for Overlapping Data in Pattern Classification”, International Journal of Fuzzy Systems, Vol. 7, No. 1, 2005.
[J4][J4]
M. R. Widyanto et al., “Unknown Odor Recognition using Euclidean Fuzzy Similarity-based Self-Organized Network Inspired by Immune Algorithm”, Neural Computing and Applications, Springer-Verlag Pub., (under review).
[J5][J5]
M. R. Widyanto et al., “Local Gas Holdup Measurement using SONIA-Ultrasonic Noninvasive Method”, Sensors & Actuator – Part A: Physical, Elsevier Science Pub., Vol. 127, No.1, 2006 (to appear) .
[J6][J6]
SONIA
F-SONIASONIA-DNN CMF-SONIA
EF-SONIA
![Page 53: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/53.jpg)
53/56
Related Publications Related Publications (2/5)(2/5)
International Conference Papers (1/2)International Conference Papers (1/2)SONIA
F-SONIASONIA-DNN CMF-SONIA
EF-SONIA
M. R. Widyanto et al., “Improvement of Artificial Odor Discrimination System using Fuzzy-LVQ Neural Network”, in the proceedings of the 3rd International Conference on Computational Intelligence and Multimedia Applications, New Delhi, India, IEEE Press, pp. 474-478, 1999.
[C1][C1]
M. R. Widyanto et al., “Clustering Analysis using a Self-Organized Network Inspired by Immune Algorithm”, in the proceedings of the IASTED International Conference on Artificial and Computational Intelligence, Tokyo, Japan, ACTA Press, pp. 197-202, 2002.
[C2][C2]
M. R. Widyanto et al., “A Time-temperature-based Food Quality Prediction using a Self-Organized Network Inspired by Immune Algorithm”, in the proceedings of the 1st International Conference on Soft Computing and Intelligent Systems, Tsukuba, Japan, 2002.
[C3][C3]
M. R. Widyanto et al., “Improvement of Three mixture Fragrances Recognition using Fuzzy Similarity based Self-Organized Network Inspired by Immune Algorithm”, in the proceedings of the 4th International Symposium on Advanced Intelligent Systems, Jeju, Island, Korea, 2003.
[C4][C4]
M. R. Widyanto et al., “Class Majority in Designing a Fuzzy Local Approximation NN”, in the proceedings of the 2nd International Conference on Soft Computing and Intelligent Systems, Yokohama, Japan, 2004.
[C5][C5]
![Page 54: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/54.jpg)
54/56
Related Publications Related Publications (3/5)(3/5)
International Conference Papers (2/2)International Conference Papers (2/2)SONIA
F-SONIASONIA-DNN CMF-SONIA
EF-SONIA
M. R. Widyanto et al., “Analysis of Fuzzy Local Approximation NN on Uncertainty Decision of Frequency Measurements”, in the proceedings of the International Symposium on Computational Intelligence and Industrial Applications, Hainan, China, 2004.
[C6][C6]
M. R. Widyanto et al., “Agent-based Decision Maker Preference Modeling Using SONIA-DNN for Restaurant Work Assignment and Scheduling Problem”, in the proceedings of the International Workshop on Agent-based Approaches in Economics and Social Complex Systems, Tokyo, Japan, 2005.
[C7][C7]
M. R. Widyanto et al., “SONIA-based Decision Neural Network and Its Application to Restaurant Work Assignment”, in the proceedings of the 6th International Symposium on Advanced Intelligent Systems, Yeosu, Korea, 2005
[C8][C8]
M. R. Widyanto et al., “Unknown Odor Category Classification using EF-SONIA”, in the proceedings of the 2nd International Symposium on Computational Intelligence and Intelligence Informatics, Hammamet, Tunisia, 2005.
[C9][C9]
M. R. Widyanto et al., “SONIA-Ultrasonic Technique for Gas Holdup Measurement of a Bubble Column”, in the proceedings of the 1st Daedeok International Conference on Human-Centered Advanced Technology, Daedeok Science Town, Korea, 2005.
[C10][C10]
![Page 55: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/55.jpg)
55/56
Related Publications Related Publications (4/5)(4/5)
Domestic Conference PapersDomestic Conference Papers
M. R. Widyanto et al., “Dealing with Incomplete Comparisons using SONIA-based Decision Neural Network”, in the proceedings of the 35-th Symposium on System Engineering, Yokohama, Japan, 2005.
[D1][D1]
SONIA
F-SONIA
SONIA-DNN CMF-SONIA
EF-SONIA
M. R. Widyanto et al., “Restaurant Work Assignment Modeling using SONIA-DNN”, in the proceedings of the 2nd Tokyo Tech COE RA Forum, Tokyo, Japan, 2005.
[D2][D2]
M. R. Widyanto et al., “Decision Preference Modeling using SONIA-DNN and Its Application to Work Assignment Problem, in the proceedings of the 21-th Fuzzy System Symposium, Tokyo, Japan, 2005.
[D3][D3]
![Page 56: Dept. of Computational Intelligence & Systems Science Tokyo Institute of Technology](https://reader033.vdocuments.mx/reader033/viewer/2022061604/5681582e550346895dc59520/html5/thumbnails/56.jpg)
56/56
Related Publications Related Publications (5/5)(5/5)
AwardsAwards
Excellent Presentation AwardExcellent Presentation AwardThe 1st International Conference on Soft Computing & Intelligent Systems,Tsukuba, Japan, September 2002.
[A1][A1]
SONIA
F-SONIA
SONIA-DNN CMF-SONIA
EF-SONIA
Gold Prize, Best Poster Award, Master Thesis PresentationGold Prize, Best Poster Award, Master Thesis PresentationDept. of Computational Intelligence & Systems Science,Tokyo Institute of Technology, Japan, February 2003.
[A2][A2]
Outstanding Paper AwardOutstanding Paper AwardThe 6th International Conference on Advanced Intelligent Systems,Yeosu, South Korea, September 2005.
[A3][A3]