Multi-Adaptive Neuro-Fuzzy Inference System for Dielectric Properties of Oil Palm Fruitlets

Ojo O. Adedayo^a*, M. M. Isa^a, A. Che Soh^a, and Z. Abbas^b 

^aDepartment of Electrical and Electronic Engineering, Faculty of Engineering, University Putra Malaysia
^bDepartment of Physics, Faculty of Science, University Putra Malaysia

REFERENCES

[1] Trabelsi, S. and Nelson, S. O. 2006. Nondestructive sensing of bulk density and moisture content in shelled peanuts from microwave permittivity measurements. Food Control, 17(4): 304-311.

[2] Sosa-Morales, M. E., Valerio-Junco, L., López-Malo, A., and García, H. S. 2010. Dielectric properties of foods: reported data in the 21st century and their potential applications. LWT-Food Science and Technology, 43(8): 1169-1179.

[3] Bohleber, P., Wagner, N., and Eisen, O. 2012. Permittivity of ice at radio frequencies: part I. coaxial transmission line cell. Cold Regions Science and Technology, 82: 56-67.

[4] Van der Vossen, H. A. M. 1974. Towards more efficient selection for oil yield in the oil palm. Centre for Agricultural Publishing and Documentation, 57-58.

[5] You, K. Y., Abbas, Z., and Khalid, K. 2010. Application of microwave moisture sensor for determination of oil palm fruit ripeness. Measurement Science Review, 10(1): 7-14.

[6] Terzic, E., Nagarajah, R., and Alamgir, M. 2011. A neural network approach to fluid quantity measurement in dynamic environments. Mechatronics, 21(1): 145-155.

[7] Erzin, Y., Rao, B. H., Patel, A., Gumaste, S. D., and Singh, D. N. 2010. Artificial neural network models for predicting electrical resistivity of soils from their thermal resistivity. International Journal of Thermal Sciences, 49(1): 118-130.

[8] Neshat, N., Mahlooji, H., and Kazemi, A. 2011. An enhanced neural network model for predictive control of granule quality characteristics. Scientia Iranica, 18(3): 722-730.

[9] Brás, S., Gouveia, S., Ribeiro, L., Ferreira, D. A., Antunes, L. and Nunes, C. S. 2013. Research in veterinary science fuzzy logic model to describe anesthetic effect and muscular influence on EEG cerebral state index. Research in Veterinary Science, 94: 735-742.

[10] Franco-Lara, E. and Weuster-Botz, D. 2007. Application of fuzzy-logic models for metabolic control analysis. Journal of Theoretical Biology, 245(3): 391-9.

[11] Hashmi, K., Graham, I., Mills, B., and Hashmi, M. S. 2003. Adjustment approach for fuzzy logic model based selection of non-overlapping machining data in the turning operation. Journal of Materials Processing Technology, 142(1): 152-162.

[12] Krajnc, D., Mele, M., and Glavič, P. 2007. Fuzzy logic model for the performance benchmarking of sugar plants by considering best available techniques. Resources, Conservation and Recycling, 52(2): 314-330.

[13] Silva, R. P. M., Delbem, A. C. B., and Coury, D. V. 2013. Genetic algorithms applied to phasor estimation and frequency tracking in PMU development. International Journal of Electrical Power & Energy Systems, 44: 921-929.

[14] Daneshfar, F. and Bevrani, H. 2012. Multiobjective design of load frequency control using genetic algorithms. International Journal of Electrical Power & Energy Systems, 42: 257-263.

[15] Mitra, A., Kundu, D., and Agrawal, G. 2006. Frequency estimation of undamped exponential signals using genetic algorithms. Computational Statistics & Data Analysis, 51(3): 1965-1985.

[16] Xu, X. and Wang, S. 2007. Optimal simplified thermal models of building envelope based on frequency domain regression using genetic algorithm. Energy and Buildings, 39(5): 525-536.

[17] Vakil-Baghmisheh, M. T., Peimani, M., Sadeghi, M. H., and Ettefagh, M. M. 2008. Crack detection in beam-like structures using genetic algorithms. Applied Soft Computing, 8(2): 1150-1160.

[18] Naderloo, L., Alimardani, R., Omid, M., Sarmadian, F., Javadikia, P., Torabi, M. Y., and Alimardani, F. 2012. Application of ANFIS to predict crop yield based on different energy inputs. Measurement, 45(6): 1406-1413.

[19] Nazari, A. and Khalaj, G. 2012. “Prediction Compressive Strength of Lightweight Geopolymers by ANFIS”. Ceramics International.

[20] Jiang, H. M., Kwong, C. K., Ip, W. H., and Wong, T. C. 2012. Modeling customer satisfaction for new product development using a PSO-based ANFIS approach. Applied Soft Computing, 12(2): 726-734.

[21] İnal, M. 2008. Determination of dielectric properties of insulator materials by means of ANFIS: a comparative study. Journal of Materials Processing Technology, 195(1-3): 34-43.

[22] Daldaban, F., Ustkoyuncu, N., and Guney, K. 2006. Phase inductance estimation for switched reluctance motor using adaptive neuro-fuzzy inference system. Energy Conversion and Management, 47(5): 485-493.

[23] İnal, M. 2008. Determination of dielectric properties of insulator materials by means of ANFIS: a comparative study. Journal of Materials Processing Technology, 195(1-3): 34-43.