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TSK神经模糊建模中规则库简化和可解释性约束学习

Int. J. Fuzzy Syst. Appl. Pub Date : 2020-04-01 DOI:10.4018/ijfsa.2020040102
Sharifa Rajab
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引用次数: 3

摘要

Neuro-fuzzysystemsbasedonafuzzymodelproposedbyTakagi，SugenoandKangknownasthe tsk_ fuzzymodelprovideapowerfulmethod formodellinguncertainandhighlycomplexnonlinearsystems。TheinitialfuzzyrulebaseinTSKneuro-fuzzysystemsisusuallyobtainedusing datadrivenapproaches。Thisprocessinducesredundancyintothesystembyaddingredundantfuzzy rulesandfuzzysets。Thisincreasescomplexitywhichadverselyaffectsgeneralizationcapabilityand transparencyofthefuzzymodelbeingdesigned。Inthisarticle，theauthorsexplorethepotentialof TSKfuzzymodelling indevelopingcomparatively interpretableneuro-fuzzysystemswithbetter generalizationcapabilityintermsofhigherapproximationaccuracy。Theapproachisbasedonthree阶段，thefirstphasedealswithautomaticdatadrivenrulebaseinductionfollowedbyrulebase simplificationphase。Rulebasesimplificationusessimilarityanalysistoremovesimilarfuzzysets andresultingredundantfuzzyrulesfromtherulebase，therebysimplifyingtheneuro-fuzzymodel。Duringthethirdphase，theparametersofmembershipfunctionsarefine-tunedusingaconstrained hybridlearningtechnique。Thelearningprocessisconstrainedwhichpreventsuncheckedupdatesto theparameterssothatahighlycomplexrulebasedoesnotemergeattheendofmodeloptimization阶段。Anempiricalinvestigationofthismethodologyisdonebyapplicationofthisapproachtotwo well-knownnon-linearbenchmark forecastingproblemsanda现实世界的stockprice预测问题。The结果表明，rulebase simplificationusinga相似度分析是有效的。removesredundancyfromthesystemwhichimprovesinterpretabilityTheremovalofredundancy alsoincreasedthegeneralizationcapabilityofthesystemmeasuredintermsofincreasedforecasting准确性。对于所有这三个预测问题，我们都演示了所提出的神经模糊系统betteraccuracy-interpretabilitytradeoffascomparedtotwowell-knownTSKneuro-fuzzymodels forfunctionapproximation。基于TSK神经模糊建模的规则库简化和可解释性约束学习
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Rule Base Simplification and Constrained Learning for Interpretability in TSK Neuro-Fuzzy Modelling
Neuro-fuzzysystemsbasedonafuzzymodelproposedbyTakagi,SugenoandKangknownasthe TSK fuzzymodelprovideapowerfulmethod formodellinguncertainandhighlycomplexnonlinearsystems.TheinitialfuzzyrulebaseinTSKneuro-fuzzysystemsisusuallyobtainedusing datadrivenapproaches.Thisprocessinducesredundancyintothesystembyaddingredundantfuzzy rulesandfuzzysets.Thisincreasescomplexitywhichadverselyaffectsgeneralizationcapabilityand transparencyofthefuzzymodelbeingdesigned.Inthisarticle,theauthorsexplorethepotentialof TSKfuzzymodelling indevelopingcomparatively interpretableneuro-fuzzysystemswithbetter generalizationcapabilityintermsofhigherapproximationaccuracy.Theapproachisbasedonthree phases,thefirstphasedealswithautomaticdatadrivenrulebaseinductionfollowedbyrulebase simplificationphase.Rulebasesimplificationusessimilarityanalysistoremovesimilarfuzzysets andresultingredundantfuzzyrulesfromtherulebase,therebysimplifyingtheneuro-fuzzymodel. Duringthethirdphase,theparametersofmembershipfunctionsarefine-tunedusingaconstrained hybridlearningtechnique.Thelearningprocessisconstrainedwhichpreventsuncheckedupdatesto theparameterssothatahighlycomplexrulebasedoesnotemergeattheendofmodeloptimization phase.Anempiricalinvestigationofthismethodologyisdonebyapplicationofthisapproachtotwo well-knownnon-linearbenchmark forecastingproblemsanda real-world stockprice forecasting problem.The results indicate that rulebase simplificationusinga similarity analysis effectively removesredundancyfromthesystemwhichimprovesinterpretability.Theremovalofredundancy alsoincreasedthegeneralizationcapabilityofthesystemmeasuredintermsofincreasedforecasting accuracy. For all the three forecasting problems the proposed neuro-fuzzy system demonstrated betteraccuracy-interpretabilitytradeoffascomparedtotwowell-knownTSKneuro-fuzzymodels forfunctionapproximation. rule Base Simplification and Constrained Learning for Interpretability in TSK Neuro-Fuzzy Modelling
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Int. J. Fuzzy Syst. Appl.
Int. J. Fuzzy Syst. Appl.
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