Chapter 2: Intelligent System Techniques
2.6 Conclusion
In this chapter, most of the well-known IS techniques that were used in these research are generally explained. These include FL, ANNs, GA, and HIS. In addition a recently developed HIS, the SGNO, was introduced. All of these techniques are powerful tools for different types of application, such as clustering, classification, forecasting or prediction, optimization and feature selection problems. Most of the techniques also have been proven to be successful in different area of study, either in linear or nonlinear applications.
52
In the following chapter, a new modelling technique will be introduced. The model is for use in general forecasting application or problems. It consists of a two- stage GA-ANN combination, which both methods to generate a model output.
References
A.S. SODIYA, S. A. O., AND B. A. OLADUNJOYE 2007. Threat Modeling Using Fuzzy Logic Paradigm. Informing Science and Information Technology, 4. B. SAFA, A. K., M. TESHNEHLAB, A. LIAGHAT 2004. Artificial neural networks
application to predict wheat yield using climatic data. International Conference.
CHEMIN, Y. & HONDA, K. 2006. Spatiotemporal Fusion of Rice Actual Evapotranspiration With Genetic Algorithms and an Agrohydrological Model. Geoscience and Remote Sensing, IEEE Transactions on, 44, 3462-3469.
DEFRA 2010. UK Food Security Asessement: Detailed Analysis. In: DEPARTMENT FOR ENVIRONMENT, F. A. R. A. (ed.). Department for Environment, Food and Rural Affairs.
EDUARD LLOBET, E. L. H., JULIAN W GARDNER AND STEFANO FRANCO 1999. Non-destructive banana ripeness determination using a neural network- based electronic nose Meas. Sci. Technol., 10.
EL-SEBAKHY, E. A., RAHARJA, I., ADEM, S. & KHAERUZZAMAN, Y. Year. Neuro-Fuzzy Systems Modeling Tools for Bacterial Growth. In: Computer Systems and Applications, 2007. AICCSA '07. IEEE/ACS International Conference on, 13-16 May 2007 2007. 374-380.
53
FAHIMIFARD, S. M., M. SALARPOUR, M. SABOUHI AND S. SHIRZADY 2009. Application of ANFIS to agricultural economic variables forecasting case study: Poultry retail price. Journal of Artificial Intelligence, 2, 65-72.
FARKAS, I., REMÉNYI, P. & BIRÓ, A. 2000. A neural network topology for modelling grain drying. Computers and Electronics in Agriculture, 26, 147- 158.
FOSTER, D., MCCULLAGH, J. & WHITFORT, T. Year. Evolution versus training: an investigation into combining genetic algorithms and neural networks. In: Neural Information Processing, 1999. Proceedings. ICONIP '99. 6th International Conference on, 1999 1999. 848-854 vol.3.
FU XIAPING, Y. Y., ET AL 2007. Principal Components-Artificial Neural Networks for Predicting SSC and Firmness of Fruits based on Near Infrared Spectroscopy. ASABE Annual meeting Paper.
GARDNER, J. W., HINES, E. L. & TANG, H. C. 1992. Detection of vapours and odours from a multisensor array using pattern-recognition techniques Part 2. Artificial neural networks. Sensors and Actuators B: Chemical, 9, 9-15.
GEMAN, S. A. B., E. 1992. Neural networks and the bias / variance dilemma. Neural Computation, 4, 1–58.
GOLDBERG, D. E. 1989. Genetic Algorithms in Search, Optimization and Machine Learning, Addison Wesley Longman, Inc.
H. DEMUTH, M. B. 2004. Neural Network Toolbox: For use with Matlab.
HOLLAND, J. H. 1992. Adaptation In Natural And Artificial Systems, MA, USA, MIT Press Cambridge.
HUEY-MING, L. 1996. Applying fuzzy set theory to evaluate the rate of aggregative risk in software development. Fuzzy Sets and Systems, 79, 323-336.
54
J W GARDNER, E. L. H. A. M. W. 1990. Application of artificial neural networks to an electronic olfactory system Meas. Sci. Technol., 1.
J.-S.R. JANG, C.-T. S., E. MIZUTANI 1997. Neuo-Fuzzy and Soft Computing, Prentice Hall.
JABBARI, A., JEDERMANN, R. & LANG, W. Year. Neural network based data fusion in food transportation system. In: Information Fusion, 2008 11th International Conference on, June 30 2008-July 3 2008 2008. 1-8.
JONES, P. Year. Networked RFID for use in the Food Chain. In: Emerging Technologies and Factory Automation, 2006. ETFA '06. IEEE Conference on, 20-22 Sept. 2006 2006. 1119-1124.
KERMANI, B. G., SCHIFFMAN, S. S. & NAGLE, H. T. 2005. Performance of the Levenberg-Marquardt neural network training method in electronic nose applications. Sensors and Actuators B: Chemical, 110, 13-22.
LOURAKIS, M. I. A. 2005. A brief description of the Levenberg-Marquardt algorithm. [Online]. Available [Accessed February 11. 2010].
M. AHMEND, E. D., ET AL. 1999. A general purpose fuzzy engine for crop control. Computational Intelligence, 1625, 473-481.
MATHWORK. 2004. Genetic Algorithm and Direct Search Toolbox.
MICHALEWICZ, Z. 1992. Genetic Algorithm + Data Structures = Evolution Programs, Springer-Verlag.
MUHD KHAIRULZAMAN ABDUL KADIR, E. H., SAHARUL AROF, DACIANA ILIESCU, MARK LEESON, ELIZABETH DOWLER, ROSEMARY COLLIER, RICHARD NAPIER, QADDOUM KEFAYA AND REZA GHAFFARI 2011. Grain Security Risk Level Prediction Using ANFIS. 3rd
55
International Conference on Computational Intelligence, Modelling and Simulation. Langkawi, Malaysia.
MUHD KHAIRULZAMAN ABDUL KADIR, E. L. H., SAHARUL AROF, DACIANA ILIESCU, MARK LEESON, ELIZABETH DOWLER, ROSEMARY COLLIER, RICHARD NAPIER, ARJUNAN SUBRAMANIAN 2012. Neural Network for Farm Household Output Prediction. International Conference on Statistics In Science, Business And Engineering. Langkawi, Malaysia.
NEGNEVITSKY, M. 2005. Artificial intelligence A guide to intelligent systems, Addison-Wesly.
ODETUNJI, O. A. & KEHINDE, O. O. 2005. Computer simulation of fuzzy control system for gari fermentation plant. Journal of Food Engineering, 68, 197-207. PERROT, N., BONAZZI, C., TRYSTRAM, G. & GUELY, F. Year. Estimation of the
food product quality using fuzzy sets. In: Fuzzy Information Processing Society, 1999. NAFIPS. 18th International Conference of the North American, Jul 1999 1999. 487-491.
POTTER, C. & NEGNEVITSKY, M. Year. ANFIS application to competition on artificial time series (CATS). In: Fuzzy Systems, 2004. Proceedings. 2004 IEEE International Conference on, 25-29 July 2004 2004. 469-474 vol.1. RANDY L. HAUPT, S. E. H. 2004. Practical Gentic Algorithm, John Wiley & Sons,
Inc.
ROSS, T. J. (ed.) 2007. Fuzzy Logic With Engineering Applications: John Wiley & Sons, Ltd.
S.A. SHEARER, T. F. B., J.P FULTON, S.F. HIGGINS 2000. Yield Prediction Using A Neural Network Classifier Trained Using Soil Landscape Features and Soil
56
Fertility Data. . In: ASAE (ed.) Annual International Meeting. Midwest Express Center, Milwaukee, Wisconsin: ASAE
TETKO, I. V., LIVINGSTONE, D.J., AND LUIK, A.I. 1995. Neural Network Studies. 1. Comparison of Overfitting and Overtraining. . J. Chem. Info. Comp. Sci., 35, 826-833.
WANG, Y.-M. & ELHAG, T. M. S. 2008. An adaptive neuro-fuzzy inference system for bridge risk assessment. Expert Systems with Applications, 34, 3099-3106. WEIGEND, A. Year. On overfitting and the effective number of hidden units. . In:
Proceedings of the 1993 Connectionist Models Summer School, 1994. 335- 342.
XIE, G., XIONG, R. & CHURCH, I. 1998. Comparison of Kinetics, Neural Network and Fuzzy Logic in Modelling Texture Changes of Dry Peas in Long Time Cooking. Lebensmittel-Wissenschaft und-Technologie, 31, 639-647.
Z. DIDEKOVA, S. K. 2009. Applications of Intelligent Hybrid Systems In Matlab. Mezinárodní konference Technical Computing. Prague.
ZHANG, F. 2011. Intelligent Feature Selection for Neural Regression Techniques and Application. Doctor of Philosophy, University of Warwick.
57