Functional Link Neural Network with Modified Artificial Bee Colony for Data Classification
نویسندگان
چکیده
FunctionalLinkNeuralNetwork(FLNN)hasemergedasanimportanttoolforsolvingnon-linear classificationproblemandhasbeensuccessfullyappliedinmanyengineeringandscientificproblems. TheFLNNstructureismuchmoremodestthanordinaryfeedforwardnetworkliketheMultilayer Perceptron (MLP)due to its flat network architecturewhich employs less tuneableweights for training.However,thestandardBackpropagation(BP)learningusesforFLNNtrainingpronetoget trapinlocalminimawhichaffecttheFLNNclassificationperformance.TorecovertheBP-learning drawback,thispaperproposesanArtificialBeeColony(ABC)optimizationwithmodificationon beeforagingbehaviour(mABC)asanalternativelearningschemeforFLNN.Thisismotivatedby goodexplorationandexploitationcapabilitiesofsearchingoptimalweightparametersexhibitbyABC algorithm.TheresultoftheclassificationaccuracymadebyFLNNwithmABC(FLNN-mABC) iscomparedwiththeoriginalFLNNarchitecturewithstandardBackpropagation(BP)(FLNN-BP) andstandardABCalgorithm(FLNN-ABC).TheFLNN-mABCalgorithmprovidesbetterlearning schemefortheFLNNnetworkwithaverageoverallimprovementof4.29%ascomparedtoFLNNBPandFLNN-ABC. KeywoRDS Artificial Bee Colony Data Mining, Functional Link Neural Network, Learning Scheme
منابع مشابه
Training a Functional Link Neural Network Using an Artificial Bee Colony for Solving a Classification Problems
Artificial Neural Networks have emerged as an important tool for classification and have been widely used to classify a non-linear separable pattern. The most popular artificial neural networks model is a Multilayer Perceptron (MLP) as is able to perform classification task with significant success. However due to the complexity of MLP structure and also problems such as local minima trapping, ...
متن کاملAn Improved Gbest Guided Artificial Bee Colony Algorithm for Classification and Prediction Tasks
Artificial Neural Networks (ANN) performance depends on network topology, activation function, behaviors of data, suitable synapse's values and learning algorithms. Researchers used different learning algorithms to train ANN for getting high performance. Artificial Bee Colony (ABC) algorithm is one of the latest successfully Swarm Intelligence based technique for training Multilayer Perceptron ...
متن کاملAn Artificial Bee Colony Inspired Clustering Solution to Prolong Lifetime of Wireless Sensor Networks
It is very difficult and expensive to replace sensor node battery in wireless sensor network in many critical conditions such as bridge supervising, resource exploration in hostile locations, and wildlife safety, etc. The natural choice in such situations is to maximize network lifetime. One such approach is to divide the sensing area of wireless sensor network into clusters to achieve high ene...
متن کاملClassification of E-Nose Aroma Data of Four Fruit Types by ABC-Based Neural Network
Electronic nose technology is used in many areas, and frequently in the beverage industry for classification and quality-control purposes. In this study, four different aroma data (strawberry, lemon, cherry, and melon) were obtained using a MOSES II electronic nose for the purpose of fruit classification. To improve the performance of the classification, the training phase of the neural network...
متن کاملGlobal Artificial Bee Colony-Levenberq-Marquardt (GABC-LM) Algorithm for Classification
The performance of Neural Networks (NN) depends on network structure, activation function and suitable weight values. For finding optimal weight values, freshly, computer scientists show the interest in the study of social insect’s behavior learning algorithms. Chief among these are, Ant Colony Optimzation (ACO), Artificial Bee Colony (ABC) algorithm, Hybrid Ant Bee Colony (HABC) algorithm and ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- IJIIT
دوره 13 شماره
صفحات -
تاریخ انتشار 2017