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1 *** ** * 87/7/4 : 8/5/7 :. 90/ (RASP) : ( mazloumzadeh@gmail.com) * ** ***
2 387 0.(5) Hyperbolic Tangent /-0/ -0- Sigmoid.(4).(7). Hyperbolic Tangent (8).( 4 7 3)....().(3 )....(3) - Artificial Neural Networ (ANN) 8
3 387 0 F( nn) = + exp( nn) ( nn. :(4) E = PN outout b p output i= ( t i o ) i (3) N P o i.... ). ( (5). t i ). (.().( ) y...m v t f ( ) w ij x i i j. m. ( w i ) ( x i ).(4) n nn = w i xi + θ i= θ Sigmoid F(nn). () ( - Multi Layer Perceptron (MLP) - Bias 83
4 387 0 : RMSE = n ~ ( A i A i ) (4 R n i= = n i= ~ ( A A ) i ( A A ) i i i (5 A i 5 4 A i i. i A ~ i 3 - Root of Mean Square Error (RMSE) 4 - Root Square (R ) 84
5 387 0 (Input layer) (Middle layer) (Output layer) - Fig.. Structure of artificial neural networ (ANN). -. ).(5 - y ( 3 ( ) - Daughter Wavelets - Mother Wavelets h a, b t -.(9 8).. ( ).. Wavelet Neural Networ (WNN) 85
6 (a) h a, b t ( ).( ) (b) E w = T t= e( h( τ ) u( ( 0 t b ( ) a h = ( ) h a, b t ( E = b E = a T t= T t=. 3 τ = E w = w e( u( w h( τ ) b h( τ ) E e( u( wτ = τ b b t b a E a = a ( ( : E b = b ( w( n + ) = w( n) + µ w w (4 b ( n + ) = b ( n ) + µ b (5 b a ( n + ) = a ( n ) + µ a ( a h(τ ).( ) yˆ ( u( t b b a t h( ) a. (a>0) : (7) ) y( = u( w h ( t = ax, bx ) (7 7. w w, ax, bx LMS 8.. ) e( = y( y( ( 8 T E = e ( ( 9 t= 0 E Least Mean Square (LMS) 8
7 Fig.. Structure of wavelet neural networ (WNN) - Table - Proposed wavelets for the application in WNN 87
8 ( ) ºC (ASAE S35.) 0 7..(5). M w = M w W w 00 (7 W W w t W t 7 ( ) ( ) )ASAE S35. (.() 88
9 387 0 Table - Effective factors on barely breaage in first experiment - ( ) Average of barley breaage (%) (m/h) Combine movement speed ( ) Barely moisture (%) ( ) Distance between thresher cylinder and concave in bac (mm) ( ) Distance between thresher cylinder and concave in front (mm) (rpm) Thresher cylinder speed Average of air temperature ( C) Date Dependent variable Based on atmosphere conditions Acceptable range (9) X X X MAX i N = (8) X MAX X MIN X i X N 8 X MAX. X MIN ).(5) ( - Feed-Forward Bacpropagation Networ
10 LM... Matlab Wavelet ANN.( 0)..(5) ) (.. - Bias ).(3 ) (. SCG 3 LM. LM. mem_reduc. LM SCG Epochs - Levenberg Marquardt 3 - Scaled Conjugate Gradient 4 - Momentum Coefficient 5 - Learning Rate 90
11 387 0 ( ). Table 3 - Results of Sensitivity Analysis - 3 RMSE Omited parameter ( C) Average of air temperature ( C) (rpm) Thresher cylinder speed (rpm) (mm) Distance between thresher cylinder and concave in front (mm) (mm) Distance between thresher cylinder and concave in bac (mm) (%) Barely moisture (%) (m/h) Combine movement speed (m/h) RMSE /9 0/ LM 0/8 Logsig Log sigmoid transfer function (Logsig) 9
12 Table 4 - Results of observed and predicted data obtained from the ANN model and WNN model : : : : ( ) (SHANNON) (RASP ) (POLYWOG ) Predicted results Observed data Predicted results (WNN) Predicted results (WNN) Predicted results (WNN) (ANN) (Breaage percentage) Prediction of breaage percentage Table 5 - Results obtained from the WNN - 5 Test RMSE R b Train Test Train Transmission b a Dilation a Neural structure wavelet type POLYWOG RASP SHANON
13 ( SHANON.(5 ). RASP - / ) --7- Table - Comparison of the results between the ANN and the WNN ANN WNN - RMSE R b Transmission a Dilation Neural Networ type Test Train Test Train b a structure (RASP) WNN ANN. SCG. LM. ( ).(5) 93
14 /95 0/9 0/04 0/ RMSE -...(7)..( ).(5).. References. ASAE (00) ASAE Standard S35.: Moisture 5. Hall JW (99) Emulating Human Process Measurement-Grain and Seeds. American Control Functions with Neural Networs. Society of Agricultural Engineers, St. Joseph, MI.. Beal R and Jacson T (998) Neural Computing: an Introduction, Institute of Physics Publishing. 3. Białobrzewsi (008) Neural modeling of relative air humidity. Computers and Electronics in Agriculture 0: Drummond ST, Suddeth KA and Birrell SJ (995) Analysis and Correlation Methods for Spatial Data. ASAE Paper ASAE, 950 Niles Rd., St. Joseph, MI Unpublished Ph.D. Dissertation. Department of Mechanical Engineering. University of Illinois, Urbana Illinois.. Iyama J and Kuwamura H (999) Application of wavelet to analysis and simulations of earthquae motions," Earth Eng. Struct Dyn 8: Kim M, Chung J and Kim C (005) Artificial neural networ approach for predicting surface water quality and quantity. Proceedings of the third Conference, 5-9 March. Atlanta. 94
15 Leutai G (997) Adaptive self-tuning neuro wavelet networ controllers. Ph.D. thesis, Virginia. 9. Mali N (005) Artificial Neural Networs and Their Applications. National Conference on Unearthing Technological Developments and their Transfer for Serving Masses. GLA ITM, Mathura, India 7-8 April. 0. Mathwors (999) MATLAB manual networ toolbox user's guide.. Matlab Software, Wavelet Toolbox, Help. Menhaj MB (998) Application of Intelligence Computation in Control. Amir Kabir Industerial University Center of Publication. Number. 3. Menhaj MB (000) Foundation of Artifitioal Neural Networs. Amir Kabir Industrial University Press, Tehran. 4. Miyamoto M and Murase H (003) Study of threshing function of combine harvester with artificial neural networ. ASAE Annual and Ghazan river basin. M.Sc. Project, Shahid Bahonar University of Kerman, Kerman, Iran.. Sanaga SA and Jain A (00) A comparative analysis of training methods for artificial neural networ rainfall runoff models. Applied Soft Computing : Stone ML (994) High Speed Networing in Construction and Agricultural Equipment. Symposium on Future Transportation Electronics: Multiplexing and In-Vehicle Networing. SAE, Warrendale PA. 8. Zheng D and Rohrbach RP (994) Neural Networs for Ultrasonic Position Control During Blueberry Pruning. ASAE Paper No ASAE, 950 Niles Rd., St. Joseph, MI Zhou B, Shi A, Cai F and Zhang Y (004) Wavelet neural networs for nonlinear time series analysis. Lecture Notes in Computer Science 374: Meeting St. Joseph, Michigan, 5. Nouri M (00) Rainfall runoff modeling with neural wavelet networ, case study: Halil River 95
16 Journal of Agriculture, Vol. 0, No., Autumn 008 Comparison of Artificial Neural and Wavelet Neural Networs for Prediction of Barley Breaage in Combine Harvester S. M. Mazloumzadeh *, S. N. Alavi ** and M. Nouri *** Abstract In this study the wavelet neural networ (WNN) and artificial neural networ (ANN) were used to simulate barley breaage percentage in combine harvester. The models have been trained using the same data conditions. Air temperature, thresher cylinder speed, distance between thresher cylinder and concave (bac and forth) and the percentage of barely moisture were as the input variables. The results showed that the wavelet networ (WNN, RASP ) with 90.% correlation coefficient for barely breaage would be an appropriate substitute for artificial neural networ with 88% correlation coefficient. The result of sensitivity analysis showed that all input variables had a significant effect on barely breaage. Speed of thresher cylinder had the most effect and the degree of air temperature had the least effect on barely breaage. Keywords: Artificial neural networ, Barley, Breaage percentage, Combine harvester, Wavelet neural networ * - Instructor, Agriculture Faculty of Saravan, University of Sistan and Balouchestan, Sistan and Balouchestan - Iran (E_mail: mymy_5370@yahoo.com) ** - Assistant Professor, Department of agricultural machinery, Shahid Bahonar Uni. of Kerman, Kerman - Iran *** - Ph.D. Student, Department of Water resource, Faculty of Agriculture, Science and Research Branch, Islamic Azad University, Tehran - Iran
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