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, over fitting and weight interference have made neural network training difficult. Thus, the easy way to avoid these problems is to remove the hidden layers. This paper presents the ability of Functional Link Neural Network (FLNN) to overcome the complexity structure of MLP by using single layer architecture and propose an Artificial Bee Colony (ABC) optimization for training the FLNN. The proposed technique is expected to provide better learning scheme for a classifier in order to get more accurate classification result.

[1]  Jagdish C. Patra,et al.  A functional link artificial neural network for adaptive channel equalization , 1995, Signal Process..

[2]  Shu-Hsien Liao,et al.  Artificial neural networks classification and clustering of methodologies and applications - literature analysis from 1995 to 2005 , 2007, Expert Syst. Appl..

[3]  Alex ChiChung Kot,et al.  Nonlinear dynamic system identification using Chebyshev functional link artificial neural networks , 2002, IEEE Trans. Syst. Man Cybern. Part B.

[4]  KarabogaDervis,et al.  A modified Artificial Bee Colony algorithm for real-parameter optimization , 2012 .

[5]  Dervis Karaboga,et al.  AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION , 2005 .

[6]  An-Sing Chen,et al.  Regression neural network for error correction in foreign exchange forecasting and trading , 2004, Comput. Oper. Res..

[7]  Dervis Karaboga,et al.  A novel clustering approach: Artificial Bee Colony (ABC) algorithm , 2011, Appl. Soft Comput..

[8]  Abir Jaafar Hussain,et al.  Dynamic Ridge Polynomial Neural Network: Forecasting the univariate non-stationary and stationary trading signals , 2011, Expert Syst. Appl..

[9]  José A. Macías,et al.  Evolution of functional link networks , 2001, IEEE Trans. Evol. Comput..

[10]  Mo-Yuen Chow,et al.  Application of functional link neural network to HVAC thermal dynamic system identification , 1998, IEEE Trans. Ind. Electron..

[11]  Hazem M. Abbas.  System Identification Using Optimally Designed Functional Link Networks via a Fast Orthogonal Search Technique , 2009, J. Comput..

[12]  Joost N. Kok,et al.  Feature Selection for Neural Networks through Functional Links Found by Evolutionary Computation , 1997, IDA.

[13]  F. Amar,et al.  Image classification in remote sensing using functional link neural networks , 1994, Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation.

[14]  Joost N. Kok,et al.  Finding Functional Links for Neural Networks by Evolutionary Computation , 2007 .

[15]  Ganapati Panda,et al.  Improved Identification of Nonlinear MIMO Plants using New Hybrid FLANN-AIS Model , 2009, 2009 IEEE International Advance Computing Conference.

[16]  Bayya Yegnanarayana,et al.  A combined neural network approach for texture classification , 1995, Neural Networks.

[17]  Issam Abu-Mahfouz,et al.  A comparative study of three artificial neural networks for the detection and classification of gear faults , 2005, Int. J. Gen. Syst..

[18]  Sung-Bae Cho,et al.  Evolutionarily optimized features in functional link neural network for classification , 2010, Expert Syst. Appl..

[19]  Guoqiang Peter Zhang,et al.  Neural networks for classification: a survey , 2000, IEEE Trans. Syst. Man Cybern. Part C.

[20]  Dervis Karaboga,et al.  Artificial Bee Colony (ABC) Optimization Algorithm for Training Feed-Forward Neural Networks , 2007, MDAI.

[21]  Yoh-Han Pao,et al.  Unconstrained word-based approach for off-line script recognition using density-based random-vector functional-link net , 2000, Neurocomputing.

[22]  B. B. Misra,et al.  Functional Link Artificial Neural Network for Classification Task in Data Mining , 2007 .

[23]  Sung-Bae Cho,et al.  A comprehensive survey on functional link neural networks and an adaptive PSO–BP learning for CFLNN , 2010, Neural Computing and Applications.

[24]  Yoh-Han Pao,et al.  Adaptive pattern recognition and neural networks , 1989 .

[25]  Y. Takefuji,et al.  Functional-link net computing: theory, system architecture, and functionalities , 1992, Computer.

[26]  Ganapati Panda,et al.  Development and performance evaluation of FLANN based model for forecasting of stock markets , 2009, Expert Syst. Appl..

[27]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[28]  Jagdish Chandra Patra,et al.  Nonlinear dynamic system identification using Legendre neural network , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[29]  Y.-H. Pao,et al.  The functional link net in structural pattern recognition , 1990, IEEE TENCON'90: 1990 IEEE Region 10 Conference on Computer and Communication Systems. Conference Proceedings.

[30]  D. Karaboga,et al.  On the performance of artificial bee colony (ABC) algorithm , 2008, Appl. Soft Comput..

[31]  M. B. Menhaj,et al.  Individual particle optimized functional link neural network for real time identification of nonlinear dynamic systems , 2010, 2010 5th IEEE Conference on Industrial Electronics and Applications.

[32]  Sung-Bae Cho,et al.  Genetic Feature Selection for Optimal Functional Link Artificial Neural Network in Classification , 2008, IDEAL.