Indian Journal of Science and Technology, Vol 9(28), DOI: 10.17485/ijst/2016/v9i28/98379, July 2016
ISSN (Print) : 0974-6846 ISSN (Online) : 0974-5645
Designing and Implementation of Neural Network using Membership Functions of Fuzzy Nishita Arora, Ashish Kr. Luhach* and Parampreet Kaur School of Computer Sciences and Engineering, Lovely Professional University, Phagwara – 144411, Punjab, India;
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Abstract Background/Objectives: An extensive literature survey has been carried out and focused on variety of application and diferentresearchareaformanydataminingtechniquesinMultinationalCompany(MNC).Methods/Statistical Analysis: Inthisresearchpaperwefindthebestclustertoextractmeaningfuldatafromlargeandcomplexpatternofneuralnetwork. This work is implemented on MATLAB Tool. By applying all the functions of research methodology we found the best cluster.ThisresearchworkiscomparedwithSOMTopologyandcanbeusedinsomanyapplicationssuchasbiometricetc. Findings:Varietyofapplicationanddiferentresearchareaareidentifiedwhichwillbehelpfulandmarkedimportantfor manydataminingtechniquesinMultinationalCompany(MNC)andlargeorganizationfordecisionmakerforallresearch resources.AmethodologyisproposedthatfocusesonzoningofneuralnetworkviamembershipfunctionofFuzzyLogic. Thismethodremovesthecomplexityofpatternextractthebestsampleeasily.Application/Improvements: In future, thismethodwillworkonbiometriclikeitwillidentifyfingerprintseasily.Itwilldividethepatternandkeepthemintothe zonesandfindthebestoneeasily.
Keywords:MembershipFunction,Neurons,Zones
1. Introduction
his section focused on Neural Network with Membership function of Fuzzy Logic. A Single neuron is not able to solve many problems so network is used. his Research paper deals with Feed Forward Neural Network. In this architecture each node is connected with every node. here are two types of connection excitatory with positive weight and inhibitory with negative weight. On the basis of this we are using Feed Forward Neural Network with membership functions of fuzzy logic. α–cut, function of fuzzy logic is used to ind the number of zones and number of rows and columns in one pattern. Ater applying similarity function on values we will get similar neurons and will make clusters and then ind out the best one. In1 focused on how a Robot interacts with human. his deals visual perception of robot with human. he purposed system contains four layers: *Author for correspondence
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Input Layer Hidden Layer Prediction Layer Output Layer
his prediction layer predicts the natural language. Robot process that language and then predict what words has been used by human and responses. his system works on dimensions and dimensionally robot extract the feature of image and natural language then match with data base and response. In2 focused on complex data by data mining techniques. hey discussed about data of inance institute, Insurance Industry‘s performance chart and some constraints of database like unique key, primary and foreign key etc. hey discussed about basics of Neural Network and advantages and Oncology of Neural network. In3 designed a framework which mines the complex knowledge. hey are performing mining on data by using classiication techniques to ind out the rules and search
Designing and Implementation of Neural Network using Membership Functions of Fuzzy
some data from tables. hey applied association Rules on the tables. hey worked on Business Rules. In4 discussed about clustering and pruning techniques. hey discussed about Divisive Artiicial Neural Network Algorithm. hey discussed about classiication and learning. Data set was taken from E. coli bacteria. hey showed the results before pruning and ater pruning and tells us the formula to ind out the number of hidden neurons. In5 focused on mining of complex data. hey worked on Resonance image of brain. hey used Fuzzy c-mean Algorithm. How our brain detects images and how our brain can remember a large amount of data. Our brain detects the image and then processes the image, it generates signal and pass this information via neurons and match with database. In6 focused on cluster analysis and SOM topology and is used membership functions of fuzzy Logic to ind out the distance between nodes by using method Euclidean distance. Equations Dj (wj , xp) = wj - xp
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Or Spherical distance: Dj (wj , xp) = 1 -wj * xp
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For adjustment of weight: w new = w old j j (1 - α ) + αx p
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In7 focused on Neural Network have very high acceptance ability, accuracy, distributed storage, high degree of fault tolerance and low error rate for noisy data. NN is good approach for data mining of data. NN is a parallel processing. For mining BP algorithm is used sometimes but there is diicult to train the parameters and due to this researcher likes to use the combination of ANN and genetic gene algorithm. It deals with Data mining based on NN. First clean the data and ater cleaning applies Rule extraction and Rule assessment are shown in Figure 1. It deals with Self-organizing Neural Network or Data mining based on Fuzzy Logic too. For better numerical output Membership Function of Fuzzy Logic is used. So Fuzzy Logic also plays a Very important role to mine the data. In8 focused on the combination of Neural Network and Genetic Algorithm that are used for clustering. his technique is used for segmentation of Magnetic Resonance images. Artiicial Neural Network is based on image analysis procedure. his approach is also used for Tongue
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Vol 9 (28) | July 2016 | www.indjst.org
Figure 1. Data mining based on neural network.
Carcinoma. Combination of Artiicial Neural Network and Genetic Algorithm is good solution of tumor of Tongue. Genetic Algorithm with Fuzzy Clustering is used for initial segmentation.
2. Proposed Methodology his Research paper, proposed method uses zoning concept to extract cluster from pattern of Neural Network. It extracts the clusters using Membership functions of fuzzy Logic. hese Membership functions consider as a weight of neurons shown in Figure 2. Cluster (n, w, f, α, row, column, sum, sum1) Step1: give input to pattern with weights. Step2: α-cut= {n, u(w)|α >= α} Step3: for (i=0 to w) Extract max (α-cut) Step4: Prime Factor (α–cut) %Find number of rows and column sum=sum+ Prime Factors; sum1=sum/2; if (sum (Prime Factors[i]
