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A study on siro, solo, compact, and conventional ring-spun yarns. Part III: modeling fiber migration usingmodular adaptive neuro-fuzzy inference systemParham Soltani a , Jamal Shahrabi b , Shahrokh Asadi b , Esmaeil Hadavandi b & Majid SafarJohari aa Department of Textile Engineering, Amirkabir University of Technology, Tehran, Iranb Department of Industrial Engineering, Amirkabir University of Technology, Tehran, IranVersion of record first published: 14 Jan 2013.

To cite this article: Parham Soltani , Jamal Shahrabi , Shahrokh Asadi , Esmaeil Hadavandi & Majid Safar Johari (2013): Astudy on siro, solo, compact, and conventional ring-spun yarns. Part III: modeling fiber migration using modular adaptiveneuro-fuzzy inference system, Journal of The Textile Institute, DOI:10.1080/00405000.2012.755295

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A study on siro, solo, compact, and conventional ring-spun yarns. Part III: modeling fibermigration using modular adaptive neuro-fuzzy inference system

Parham Soltania*, Jamal Shahrabib, Shahrokh Asadib, Esmaeil Hadavandib and Majid Safar Joharia

aDepartment of Textile Engineering, Amirkabir University of Technology, Tehran, Iran; bDepartment of Industrial Engineering,Amirkabir University of Technology, Tehran, Iran

(Received 2 February 2012; final version received 29 November 2012)

The importance of fiber migration in spun yarns as a means of securing cohesion and strength has been empha-sized in the literature. However, analyzing migration behavior of fibers is a time-consuming and tedious task. Athree-stage hybrid model was developed to estimate yarn migratory properties based on some physical andmechanical properties of spun yarns. Achieving the objectives of this research, general physical, mechanical, andstructural properties of spun yarns together with existing standards were thoroughly studied. At the first stage,using stepwise regression analysis, key variables were selected. At the second stage, data-set was clustered intosubpopulations by means of K-means in order to decrease effects of noise, rebate complexity of the patterns, anddevelop a modular model. At the third stage, using adaptive neuro-fuzzy inference system, the target value waspredicted. Finally, evaluation of the proposed model was carried out by applying it on the test set.

Keywords: migration factor; yarn tenacity; yarn hairiness; yarn structure; modular adaptive neuro-fuzzy inferencesystem

Introduction

The mechanical and physical properties of spun yarnsand fabrics such as yarn bulkiness, tensile behavior,and abrasion resistance depend not only on propertiesof constituent fibers, but also on the yarn structurecharacterized by geometrical arrangement of fibers inthe yarn body. Knowledge of how fiber characteristicsaffect yam properties is, therefore, of considerableimportance. The structural analysis of yarns, particu-larly, migration of fibers, has widely reported in theliterature and continues to be of interest to manyresearchers. Gupta and Hearle (1965), Hearle, Gupta,and Merchant (1965), and Treloar and Riding (1965),to name a few, made important contribution on thiscontroversial topic. Various methods have been pro-posed by researchers to investigate yarn inner structure(Basal, 2003; Morton, 1956; Schwarz, 1951). How-ever, these methods are extremely an exhausting andtime-consuming task.

The internal structure of spun yarns is quitecomplicated due to the complex nature of severaldeterministic and random phenomena. Recently, therehas been a growing interest in using artificialintelligence (AI) models in textile applications(Gharehaghaji, Shanbeh, & Palhang, 2007; Guha,Chattopadhyay, & Jayadeva, 2001; Majumdar &

Majumdar, 2004; Ramesh, Rajamanickam, & Jayar-aman, 1995; Yao, Guo, & Zhou, 2005). AI techniquessuch as artificial neural networks, fuzzy logic, andgenetic algorithms (GAs) are popular research subjects,since they can deal with complex engineeringproblems which are difficult to solve by classicalmethods (Konar, 2005). These techniques have beensuccessfully used in prediction and control problems(Hadavandi, Shavandi, & Ghanbari, 2011). AI tech-niques can be combined together in various ways toform hybrid models. Hybrid models have moreflexibility and can be used to estimate the nonlinearrelationship, without the limits of traditional modelssuch as regression (Hadavandi, Shavandi, & Ghanbari,2010a). Therefore, more and more researchers tend touse hybrid prediction models to deal with predictionand control problems.

In the literature, the fuzzy set theory has beenapplied to develop prediction models. It provides alanguage by which indefinite, imprecise, and incom-plete factors can be captured (Kahraman, Yavuz, &Kaya, 2010). The structure of fuzzy models is ofteneasier while it is more realistic than nonfuzzy andmodels which tend to add layers of complexity andattempt to formulate an imprecise underlying structure(Zarandi, Hadavandi, & Turksen, 2012). Fuzzy logic

*Corresponding author. Email: parham-aut@aut.ac.ir

The Journal of The Textile Institute, 2013http://dx.doi.org/10.1080/00405000.2012.755295

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offers a better way to represent complicated situationsin terms of simple natural language and has beenapplied very practically in many fields where classicalmodel-based approaches are difficult to implement forthe design and learning (Hadavandi, Shavandi,Ghanbari, & Abbasian, 2012). In this paper, the fuzzyset theory is used to develop the efficiency, accuracy,and sensitiveness of prediction models.

Using modular approaches is one of the best waysto improve accuracy of a prediction model. In modularmodels, a task or problem is decomposed into a num-ber of subtasks and each module handles a subtask ofthe global task (Auda & Kamel, 1999). Data cluster-ing has been applied in various studies to divide thedata into subpopulations and reduce the complexity ofthe whole data space to something more homogeneousand reduce the effects of noisy data (Zarandi et al.,2012). All have reported that using the data clusteringmodel improves prediction accuracy.

Despite intensive researches on prediction of vari-ous yarn properties, we know of hardly any publishedreport that encompasses the scope of estimating fibermigration in spun yarns using physical and mechanicalproperties of spun yarns. More recently, in a novelapproach, Grishanov, Siewe, and Cassidy (2011), andSiewe, Grishanov, Cassidy, and Banyard (2009) estab-lished a queuing model of staple fiber yarn where themigration behavior of fibers was simulated by themovement of customers in a Markovian network ofqueuing systems. A multi-dimensional minimizationmethod for estimating the migration probabilities offibers based on the analysis of the distribution of num-ber of fibers in yarn cross-sections was applied. How-ever, the results exhibited a poor agreement betweenthe predicted packing density and the average numberof fibers in the yarn cross-section with the experimen-tal data. It could be ascribed to the fact that the exper-imentally estimated migration probabilities were notunconditional probabilities as required by the model.

As mentioned previously, analyzing migrationbehavior of fibers is a time-consuming and arduoustask. Therefore, our objective at the present paper is todevelop a hybrid AI model which is a combination ofvariable selection, data clustering, and adaptive neuro-fuzzy inference system (ANFIS), to estimate migratorybehavior of fibers using some measured yarn physicaland mechanical properties. The hybrid model usesstepwise regression analysis (SRA) for selection ofinput variables to improve prediction accuracy.

Variables selection by SRA

Variable selection is the technique of selecting an opti-mum subset of predictors from the set of potentiallyuseful variables which may be available in a given

problem. There are many potential benefits of variableselection: facilitating data visualization and dataunderstanding, reducing the measurement and storagerequirements, reducing training and utilization times,and defying the curse of dimensionality to improveprediction performance. Various feature selectionmethods such as GA (ElAlami, 2009), principal com-ponent analysis, and SRA have been applied by differ-ent researchers to select significant factors inprediction systems (Zhang, 2007). Many authors havediscussed and evaluated different methods of variableselection. Raffalovich, Deane, Armstrong, and Tsao(2008) provide an excellent review of work done inthis area. Among the variable selection methods, inrecent years, some researchers have used SRA forinput variable selection and they have obtained verypromising results (Hadavandi, Shavandi, & Ghanbari,2010b). The present paper adopts stepwise regressionto analyze and select variables, as its consequenceimprove the prediction accuracy of the system.

Using stepwise regression method, the set of inde-pendent factors that most closely determine the depen-dent variable is determined. In this method, candidatepredictors are screened for possible inclusion and vari-ables already in the model are considered for possibleremoval in a sequential fashion. For each step, simpleregression is performed using the previously includedindependent variables and one of the excluded variables.

The criterion for adding or removing is determinedby F-test statistic value and decreasing the sum ofsquared error. After the entrance of first variable tothe model, the variable number is increased step bystep; once it is removed from this model, it will neverenter the model again. Before selecting variables, thecritical point, level of significance, and the values ofFe (F-to-enter) and Fr (F-to-remove) have to be deter-mined first. Then, the partial F value of each step hasto be calculated and compared to Fe and Fr: if F>Fe,it is considered to add variables to the model; other-wise, if F<Fr, the variables are removed from model(Burkholder & Lieber, 1996). The statistical softwareSPSS 17.0 was used to applying SRA in this researchconsidering Fe ¼ 3:84 and Fr ¼ 2:71.

Data clustering by K-means

Clustering algorithms are classified into two groups:the agglomerative hierarchical algorithms such as thecentroid and Ward methods, and the nonhierarchicalclustering, such as K-means and self-organizing map(SOM) neural networks (Anderberg, 1973). Each ofthese algorithms has their own advantages and disad-vantages. Depending on the application, a particulartype of clustering method should be chosen. Amongclustering algorithms, because of simplicity and fast

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convergence of K-means, it has been used in a widerange of applications (Nikham & Amiri, 2010).

The K-means method is a technique employed forpartitioning a set of objects into k groups such thateach group is homogeneous with respect to certainattributes based on the specific criterion (Anderberg,1973). We use K-means data clustering to divide thedata into subpopulations and reduce complexity of thewhole data space, decompose complicated patterns toa set of simple patterns, and decrease effects of noiseby focusing on similar data in each cluster and conse-quently, increase the model accuracy.

Given a set of observations (x1,x2,…,xn), whereeach observation is a dimensional real vector, K-means clustering aims to partition the observationsinto sets (k < n), the procedure of K-means method issummarized below:

Step 1: Randomly select k initial cluster cen-troids, where k is the number of the clusters.Step 2: Assign each object to the cluster towhich it is the closest based on the distancebetween the object and the cluster mean (weuse Euclidean metric as the distance function).

d(xi;mi) ¼ kxi � mik ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiXd

k¼1

(xik � mjk)2

vuut

where xi is the ith observation, mj is centroid ofthe jth cluster, and d is dimension of input vectors.Step 3: Calculate the new mean for each clusterand reassign each object to the cluster.Step 4: Stop if the criterion converges (thealgorithm is deemed to have converged whenthe assignments no longer change). Otherwise,go back to Step 2.

ANFIS

A fuzzy rule-based system (FRBS) is an applicationof fuzzy logic to rule-based systems. An FRBS

consists of four blocks, namely fuzzifier, defuzzifier,fuzzy inference engine, and fuzzy rule base as shownin Figure 1.

The fuzzifier in the FRBS maps a crisp point x inU into a fuzzy set in U. The fuzzy inference enginesystem maps input fuzzy sets into output fuzzy sets bymeans of an inference mechanism. The output of theinference engine is a fuzzy set characterized by themembership values. To use this result in a real worldproblem, it must be converted into a real number. Thisprocess is called defuzzification (for more details seeCordón, Herrera, Hoffmann, & Magdalena (2001)).

The FRBS can be classified into three broad types,namely the linguistic (Mamdani-type), the relationalequation, and the Takagi–Sugeno–Kang (TSK). Inlinguistic models, both the antecedent and the conse-quence are fuzzy sets while in the TSK model, theantecedent consists of fuzzy sets but the consequenceis made up of linear equations. Fuzzy relational equa-tion models aim at building the fuzzy relation matricesaccording to the input–output process data-set (Wang,1997).

A fuzzy rule in Mamdani-type FRBS and for afirst-order TSK-type, FRBS is represented as follows:

Mamdani rule: If X1 is A1 and X2 is A2, then Y is C1.

TSK rule: If X1 is A1 and X2 is A2, thenY ¼ b0 þ b1X1 þ b2X2:

where X1, X2, and Y are linguistic variables, A1, A2,and C1 are corresponding fuzzy sets, and b0, b1, andb2 are linear parameters.

In order to design an intelligent FRBS for a con-crete application, these four blocks must be speci-fied. One of the most important and difficult ones isto derive an appropriate knowledge base (KB) aboutthe problem. The KB stores the available knowledgein the form of fuzzy linguistic IF–THEN rules. It iscomposed of the rule base (RB), constituted by thecollection of rules in their symbolic forms, andthe data base, which contains the linguistic term sets

Fuzzifier Defuzzifier

Fuzzyinferenceengine

Fuzzy ruleBase

x in U

Fuzzy sets in U Fuzzy sets in V

y in V

Figure 1. FRBSs with fuzzifier and defuzzifier.

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and the membership functions (MFs) defining theirmeanings (Casillas, Cordón, Herrera, & Villar,2004).

The difficulty presented by human experts toexpress their knowledge in the form of fuzzy rules hasmade researchers develop automatic techniques to per-form this task (Hadavandi, Ghanbari, Shahanaghi, &Abbasian, 2011). In this sense, a large amount ofmethods has been proposed to automatically generatefuzzy rules from numerical data. Usually, they usecomplex rule generation mechanisms such as neuralnetworks (Nauck, Klawonn, & Kruse, 1997) or GAs(Cordón et al., 2001; Hadavandi et al., 2010b).

The ANFISs are fuzzy Sugeno models put in theframework of adaptive systems to facilitate learningand adaptation. Such framework makes fuzzy systemsmore systematic and less relying on expert knowledge(Hadavandi et al., 2010a). To present the ANFISarchitecture, let us consider two fuzzy rules based ona first-order Sugeno model:

Rule 1: If x is A1 and y is B1, then f1 = p1x + q1y+ r1.

Rule 2: If x is A2 and y is B2, then f2 = p2x + q2y+ r2.

One possible ANFIS architecture to implementthese two rules is depicted in Figure 2. Note that a cir-cle indicates a fixed node, whereas a square indicatesan adaptive node (the parameters change during train-ing). In the following presentation, OLi denotes theoutput of node i in a layer L.

Layer 1: All the nodes in this layer are adaptivenodes, i is the degree of the membership of the inputto the fuzzy MF represented by the node:

O1;i ¼ lAi(x) i ¼ 1; 2

O1;i ¼ lBi�2(y) i ¼ 3; 4

Ai and Bi can be any appropriate fuzzy sets inparameter form. For example, if bell MF is used, then:

lAi(x) ¼ 1

1þ ( x�ciai

)2h ibi i ¼ 1; 2

where ai, bi, and ci are the parameters for the MF.

Figure 2. General framework of ANFIS training model.

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Layer 2: The nodes in this layer are fixed (notadaptive). These are labeled M to indicate that theyplay the role of a simple multiplier. The outputs ofthese nodes are given by:

O2;i ¼ wi ¼ lAi(x)lBi(y) i ¼ 1; 2

The output of each node in this layer representsthe firing strength of the rule.

Layer 3: Nodes in this layer are also fixed nodes.These are labeled N to indicate that these perform anormalization of the firing strength from previouslayer. The output of each node in this layer is givenby:

O3;i ¼ �wi ¼ wi

w1 þ w2i ¼ 1; 2

Layer 4: All the nodes in this layer are adaptivenodes. The output of each node is simply the productof the normalized firing strength and a first-order poly-nomial:

O4;i ¼ �wi f i ¼ �wi(pixþ qiyþ ri) i ¼ 1; 2

where pi, qi, and ri are consequent parameter sincethey deal with the then-part of the fuzzy rule.

Layer 5: This layer has only one node labeled S toindicate that it performs the function of a simple sum-mer. The output of this single node is given by:

O5;i ¼ f ¼Xi

�wi f i ¼P

i wi fiPi wi

i ¼ 1; 2

The ANFIS architecture is not unique (Jang &Mizutani, 1997). Some layers can be combined andstill produce the same output. In this ANFIS architec-ture, there are two adaptive layers (1 and 4). Layer 1has three modifiable parameters (ai, bi, and ci) pertain-ing to the input MFs. These parameters are called pre-mise parameters. Layer 4 has also three modifiableparameters (pi, qi, and ri) pertaining to the first-orderpolynomial. These parameters are called consequentparameters. General framework of the proposed modelis shown in Figure 3.

Experimental

Yarn properties measurement

Staple (38mm, 1.6 denier) lyocell fibers were pro-cessed to produce a drawn sliver of 2.6 ktex. Tracerfibers were added to undyed lyocell fibers during the

Fuzzy input

Defuzzification

Error measurement

Wei

ght a

djus

tmen

t

Stopping criteriasatisfied?

Training ends

Yes

No

START

Variables selection by stepwise regression

Data clustering using K-means

STOP

Trained fuzzy system

Figure 3. Architecture of the proposed model.

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opening stage which was followed by carding, twostages of drawing, roving, and spinning. The yarnswere conditioned at 65 ± 2% relative humidity (RH)and 24 ± 2°C for 24 h and subsequently, tested forphysical and mechanical properties.

The linear density to be spun, the machine, themachine setting, and the measurements of physicaland mechanical properties of yarns were the same asin our previous research (Soltani & Johari, 2012b,2012c).

Implementing neuro-fuzzy systems

A broad range of yarn properties including hairiness,Uster evenness, number of thin and thick places,spinning-in-coefficient, percentage of straight fibers,proportion of broken fibers, and tenacity were mea-sured for compact and conventional ring-spun yarns atdifferent twist multipliers. SRA is utilized to eliminatelow impact factors and choose the most dominantones out of mentioned factors. The outcomes of thisstage were hairiness, tenacity, and proportion of bro-ken fibers. In the second stage, K-means is used todivide the data into subpopulations in order todecrease effects of noise and rebate complexity of thepatterns. We use trail-and-error process for determin-ing the size of number of cluster. After examination ofdifferent number of clusters such as 2, 3, and 4, weobtained that in our cases, the suitable number ofclusters (k) that provides minimum mean absolutepercentage error (MAPE) values for test data ofconventional ring and compact-spun yarns is 2.Figures 4 and 5 visualize the two clusters of compactand ring-spun yarns data.

These two figures show that K-means clusteringalgorithm can be a very effective technique to identifynatural groupings in our data, thereby allowing con-cise representation of relationships embedded in the

data for developing modular predictors. In our study,clustering allows us to group yarn migration processbased on the three considered factors; hence, allowingfor easier understandability.

In the third stage, for each cluster, a neuro-fuzzysystem is developed to estimate yarn migratory param-eters using the training data-set in that cluster. The90% of data-set as training sets and the remaining10% as test set are randomly selected. Finally, in thetesting phase, the data are first clustered and predic-tion of yarn migration is done by means of each clus-ter’s test data.

It should be noted that different combinations ofparameters have been examined to find suitableparameters of ANFIS model with the least error.Tuned combination of ANFIS’s parameters for eachcluster is shown in Table 1.

Results and discussion

Evaluating prediction accuracy of proposed modularANFIS model was performed by a common evaluationstatistic called MAPE:

Table 1. Suitable parameter values of ANFIS model.

Spinningsystem Parameters Cluster 1 Cluster 2

Ring-spunyarn

# of epochs 110 125Error tolerance (MSE) 10�5 10�5

# of MF’s (Input layer) 3 2MF type (Input layer) Gaussian GbellMF type (Output layer) Linear Linear

Compact-spun yarn

# of epochs 100 115Error tolerance (MSE) 10�5 10�5

# of MF’s (Input layer) 2 2MF type (Input layer) Gbell GbellMF type (Output layer) Linear Linear

Figure 4. K-means clustering results for compact-spunyarns data.

Figure 5. K-means clustering results for conventionalring-spun yarns data.

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MAPE ¼ 100� 1

N

XNi¼1

APEi

APEi ¼ jYi � PijYi

where Yi is the actual value and Pi is the predictedvalue of ith test data obtained from proposed modeland N is number of test data. Best value for MAPE iszero and being much closer to zero, it indicates thatprediction accuracy is higher. Lower the value ofMAPE, more desired results from the predictionmethod.

Table 2. Prediction results of modular ANFIS model for test data of conventional ring-spun yarns.

Hairiness Proportion of broken fibers Tenacity Migration factor Predicted value by ANFIS APE MAPE

6.36 66 13 0.3032 0.304 0.26385226.36 63 12.84 0.2977 0.3012 1.17568026.37 64 12.9 0.2956 0.3031 2.53721246.39 64 12.87 0.2946 0.292 0.88255266.39 62 12.76 0.2909 0.288 0.9969062 1.1646.4 59 12.55 0.2836 0.289 1.90409036.41 58 12.5 0.28195 0.2795 0.86894846.42 57 12.45 0.28024 0.2805 0.09277766.45 54 12.28 0.2744 0.267 2.6967936.45 52 12.14 0.2696 0.26898 0.2299703

Table 3. Prediction results of modular ANFIS model for test data of compact-spun yarns.

Hairiness Proportion of broken fibers Tenacity Migration factor Predicted value by ANFIS APE MAPE

6.4 52 12.94 0.288 0.289 0.3472222226.44 51 12.86 0.286 0.275 3.8461538466.46 51 12.79 0.283 0.301 6.3604240285.67 69 13.29 0.453 0.451 0.4415011045.7 68 13.21 0.45 0.461 2.444444444 1.8065.9 66 13.81 0.481 0.474 1.4553014555.91 64 13.67 0.476 0.4798 0.7983193286.24 58 13.73 0.509 0.513 0.7858546176.27 56 13.58 0.505 0.502 0.5940594066.3 55 13.52 0.504 0.509 0.992063492

Figure 6. Actual values vs. predicted value of migration factor.

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The prediction results of the proposed model forthe test data are listed in Tables 2 and 3 and shown inFigure 6.

As can be observed, the results indicate that imple-mented modular ANFIS models enjoy good predictionaccuracy. Also, the Rule Base of modular ANFIS

model for cluster 1 of conventional ring-spun yarnsdata is shown in Figure 7.

The obtained MFs in Figure 7 represent thelinguistic terms characterized by fuzzy sets rather thanquantity terms. For example, the exposure level of inputvariables is expressed linguistically as ‘low’, ‘moderate’

Proportion of broken fibers Tenacity Migration factorHairiness

Figure 7. RB of the fuzzy systems of conventional ring-spun yarns for cluster 1.

Figure 8. The schematic of neuro-fuzzy system of conventional ring-spun yarns for cluster 1.

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and ‘high’. These linguistic variables with noncrispinformation are consistent with the imprecise and uncer-tain nature, while traditional quantitative analyses donot address the issue of such imprecision. We can usethe obtained fuzzy rule sets to simulate the relationshipsamongst yarns migratory properties and consideredphysical and mechanical properties of spun yarns.

The schematic of trained ANFIS model for cluster1 of conventional ring-spun yarns data are shown inFigure 8.

The simulated response surface of tenacity, propor-tion of broken fibers, and migration factor for conven-tional ring-spun yarn is presented in Figure 9. As canbe seen, fiber migration increases with both tenacityand proportion of broken fibers. When a spun yarn issubjected to uniaxial loading, the applied stress is dis-

tributed among the fibers. When the applied loadexceeds the tenacity of some of the weaker fibers,they yield and yarn rupture process is initiated. Theinitiated rupture propagates rapidly if the surroundingstructure of stronger fibers does not offer adequateresistance by its arrangement in the yarn body (lowermigration factor), hence reducing the structuralsupport to other unbroken fibers and intensifying theslippage of some of unsupported fibers. As a result,the load-bearing capacity of yarn structure as a wholereduces and yarn tenacity diminishes. Higher migra-tion factor indicates that the yarn forms a coherentself-locking structure in which fiber slippage isrestricted. The tensile deformation of such a yarn isdominated by fiber breakage rather than by weakeningof the structure due to fiber slippage, thereby increas-ing yarn strength (Anandjiwala, Bargeron, Bragg, &Goswami, 1999; Soltani & Johari, 2012a, 2012c).

The results of modular ANFIS model were com-pared with multi-layer feed forward neural networkswith the best obtained architecture 3–5–1. In order toinvestigate the effects of modularizing prediction on theimprovement of ANFIS, we implemented it withoutmodularizing (without clustering). The results of predic-tion accuracy for test data are presented in Figure 10.

Referring to Figure 10, ANFIS with clustering hasoutperformed ANFIS and neural network model basedon the comparison statistic and can be considered as apromising alternative for spun yarn’s migration predic-tion problems.

Conclusion

The specific regulations of internal yarn geometry arerelatively complicated due to the complex nature of

Tenacity Proportion of broken

fibers

Migrationfactor

Figure 9. Simulated response surface of tenacity, proportion of broken fibers, and migration factor.

Figure 10. MAPE comparison between the proposedmodel, ANFIS without clustering, and multilayer perceptron(MLP) neural network.

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several deterministic and random phenomena. Theradial migration of fibers is one of those phenomena.Current methods of analyzing migration behavior offibers are very time-consuming and tedious. Therefore,in the present paper a new approach to estimatemigratory behavior of spun yarns was established.This paper presented a hybrid AI based on an ANFIS,K-means clustering, and SRA variable selectionmethod for constructing an intelligent spun yarnmigratory estimator. At the first stage, key variableswere selected using SRA. Afterwards, using K-meansalgorithm, data-set was clustered into subpopulationsin order to decrease the effects of noise and rebatecomplexity of the patterns and develop a modularmodel. Finally, ANFIS was implemented to predictyarn migration factor for each cluster. Results demon-strated that prediction accuracy of implementedmodular ANFIS with clustering models was very goodand it can be used as an intelligent estimator to dealwith fiber migration prediction and control.

Moreover, controllers are capable of handling non-linearity, complexity as well as uncertainty that mayexist in actual data-sets with respect to modelingmigration factor due to erratic responses and measure-ment errors. The proposed model will provide control-lers with improved prediction and decreased error incomplex and uncertain environment. We can beassured of working with optimum solutions, expressedin an easy, semantically understandable way of reason-ing of the human being.

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