Roll of Membership functions in Fuzzy Logic for Prediction of Shoot Length of Mustard Plant Based on Residual Analysis
The selection for plantation of a particular type of
mustard plant depending on its productivity (pod yield) at the stage
of maturity. The growth of mustard plant dependent on some
parameters of that plant, these are shoot length, number of leaves,
number of roots and roots length etc. As the plant is growing, some
leaves may be fall down and some new leaves may come, so it can
not gives the idea to develop the relationship with the seeds weight at
mature stage of that plant. It is not possible to find the number of
roots and root length of mustard plant at growing stage that will be
harmful of this plant as roots goes deeper to deeper inside the land.
Only the value of shoot length which increases in course of time can
be measured at different time instances. Weather parameters are
maximum and minimum humidity, rain fall, maximum and minimum
temperature may effect the growth of the plant. The parameters of
pollution, water, soil, distance and crop management may be
dominant factors of growth of plant and its productivity. Considering
all parameters, the growth of the plant is very uncertain, fuzzy
environment can be considered for the prediction of shoot length at
maturity of the plant. Fuzzification plays a greater role for
fuzzification of data, which is based on certain membership
functions. Here an effort has been made to fuzzify the original data
based on gaussian function, triangular function, s-function,
Trapezoidal and L –function. After that all fuzzified data are
defuzzified to get normal form. Finally the error analysis
(calculation of forecasting error and average error) indicates the
membership function appropriate for fuzzification of data and use to
predict the shoot length at maturity. The result is also verified using
residual (Absolute Residual, Maximum of Absolute Residual, Mean
Absolute Residual, Mean of Mean Absolute Residual, Median of
Absolute Residual and Standard Deviation) analysis.
[1] Q. Song and B. S. Chissom, "Forecasting enrollments with fuzzy time
series part I", Fuzzy Sets and Systems 54(1993) 1 - 9.
[2] J. Sullivan and William H. Woodall, "A Comparison of Fuzzy
Forecasting and Markov Modelling", Fuzzy Sets and Systems 64(1994)
279 - 293.
[3] Q. Song and B. S. Chissom, "Fuzzy Time Series and its Models", Fuzzy
Sets and Systems 54(1993) 269-277.
[4] H. Bintley, "Time Series analysis with Reveal", Fuzzy Sets and Systems
23(1987) 97-118.
[5] Q. Song and B. S. Chissom, "Forecasting enrollments with fuzzy time
series - part II", Fuzzy Sets and Systems 62(1994) 1-8.
[6] G. A. Tagliarini, J. F. Christ, E. W. Page, "Optimization using Neural
Networks, IEEE Transactions on Computers. vol. 40. no 12. December
ÔÇÿ91 1347-1358
[7] T. K. Bhattacharya and T. K. Basu, "Medium range forecasting of power
system load using modified Kalman filter and Walsch transform",
Electric Power and Energy Systems", vol. 15, no 2, 109 -115, 1993.
[8] F. G. Donaldson and M. Kamstra, "Forecast combining with Neural
Networks", Journal of Forecasting 15(1996) 49-61.
[9] J. V. Hansen and Ray D. Nelson, "Neural Networks and Traditional
Time Series Methods: A Synergistic combination in state Economic
Forecasts", IEEE Transactions on Neural Networks vol. 8, no 4, July
1997.
[10] S. F. Brown, A. J. Branford, W. Moran, "On the use of Artificial Neural
Networks for the analysis of Survival Data", IEEE Transactions on
Neural Networks, vol. 8, no 5, Sept. 1997.
[11] M. Sugeno and K. Tanaka, "Successive Identification of a Fuzzy Model
and its applications to prediction of a complex system", Fuzzy Sets and
Systems 42(1991) 315-334.
[12] L. Zuoyong, Chen Zhenpei, Li Jitao, "A model of Weather Forecast by
Fuzzy Grade Statistics", Fuzzy Sets and Systems 26(1998) 275-281.
[13] L. feng and Xu Xia Guang, "A Forecasting Model of Fuzzy Self
Regression", Fuzzy Sets and Systems 58(1993) 239-242.
[14] M. Ishikawa, T. Moriyama, "Prediction of Time Series by a Structural
Learning of Neural Networks", Fuzzy Sets and Systems 82(1996) 167-
176.
[15] D.H.Goldberg, "Genetic Algorithms in Search, Optimization and
Machine Learning", Addison Wesley Inc, 1999.
[16] J. Paul Choudhury, Dr. Bijan Sarkar and Prof. S. K. Mukherjee, "Some
Issues in building a Fuzzy Neural Network based Framework for
forecasting Engineering Manpower", Proceedings of 34th Annual
Convention of Computer Society of India, Mumbai, pp. 213-227,
October -November 1999.
[17] J. Paul Choudhury, Dr. Bijan Sarkar and Prof. S. K. Mukherjee, "Rule
Base of a Fuzzy Expert Selection System", Proceedings of 34th Annual
Convention of Computer Society of India, Mumbai, pp. 98-104, October
-November 1999.
[18] J. Paul Choudhury, Dr. Bijan Sarkar and Prof. S. K. Mukherjee, "A
Fuzzy Time Series based Framework in the Forecasting Engineering
Manpower in comparison to Markov Modeling", Proceedings of
Seminar on Information Technology, The Institution of
Engineers(India), Computer Engineering Division, West Bengal State
Center, Calcutta, pp. 39-45, March 2000.
[19] G. P. Bansal, A. Jain, A. K. Tiwari and P. K. Chanda, "Optimization in
the operation of Process Plant through Genetic Programming", IETE
Journal of Research, vol 46, no 4, July-August 2000, pp 251-260.
[20] K. K. Shukla, Neuro-genetic prediction of Software Development
Effort", Information and Software Technology 42(2000), pp 701-713.
[21] S. Bandyopadhaya and U. Maulik, "An Improved Evolutionary
Algorithm as Function Optimizer", IETE Journal of Research, vol 46, no
1 and 2, pp 47-56, 2000.
[22] B. Banerjee, A. Konar and S. Mukhopadhayay, " A Neuro-G.A approach
for the Navigational Planning of a Mobile Robot", Proceedings of
International Conference on Communication, Computers and
Devices(ICCD-2000), Department of Electronics and Electrical
Engineering, Indian Institute of Technology, Kharagpur, December
2000, pp 625-628.
[23] J. Paul Choudhury, Dr. Bijan Sarkar and Prof. S. K. Mukherjee,
"Forecasting using Time Series Model Direct Method in comparison to
Indirect Method", Proceedings of International Conference on
Communication, Computers and Devices(ICCD-2000), Department of
Electronics and Electrical Engineering, Indian Institute of Technology,
Kharagpur, December 2000, pp 655-658.
[24] G. W. Snedecor and W.G.Cochran, "Statistical Methods", eight edition,
East Press, 1994.
[25] Dr. J. Paul Choudhury, Satyendra Nath Mandal, Prof Dilip Dey, Prof. S.
K. Mukherjee, Bayesian Learning versus Neural Learning : towards
prediction of Pod Yield", Proceedings of National Seminar on Recent
advances on Information Technology(RAIT-2007), Department of
Computer Science and Engineering, Indian School of Mines University,
Dhanbad, India , pp 298-313, February 2007.
[26] Dr. J. Paul Choudhury, Satyendra Nath Mandal, Prof. S. K. Mukherjee,
"Shoot Length Growth Prediction of Paddy Plant using Neural Fuzzy
Model", Proceedings of National Conference on Cutting Edge
Technologies in Power Conversion & Industrial Drives PCID,
Department of Electrical and Electronics Engineering , Bannari Amman
Institute of Technology.
[27] J. Paul Choudhury, Satyendra Nath Mandal, Prof Dilip Dey, Prof. S. K.
Mukherjee, "Soft Computing model: towords the forecasting of pod
yield as compared to Statistical Model", International Conference on
Information Technology , Haldia,March 2007.
[28] Satyendra Nath Mandal, J. Paul Choudhury, Prof Dilip Dey, Prof. S. R .
Bhadra Chaudhuri ," Growth Estimation With Artificial Neural
Network Considering Weather parameters Using factor and Principal
Component Analysis", CD Proceeding , 10th International Conference on
Information Technology,17-20 December, 2007,Rourkela,India.
[29] Satyendra Nath Mandal, J. Paul Choudhury, Prof Dilip Dey, Prof. S. R .
Bhadra Chaudhuri ," Growth Estimation With simulated Annealing
Considering Weather parameters Using factor and Principal Component
Analysis", Proceeding National Conference on Methods and Models in
Computing (NCM2C 2007),pp184-197,JNU, New Delhi, India.
[30] Satyendra Nath Mandal, J. Paul Choudhury, Prof Dilip Dey, Prof. S. R.
Bhadra Chaudhuri ," A Frame Work for the Development of a Suitable
Method to Find Shoot Length at Maturity of Mustard Plant Using Soft
Computing Model", publish in "International Journal of Computer
Science & Engg.", Vol2. No.3, 2008.
[31] Thierry Denoeux, Member, IEEE, and Marie-Hélène Masson,"Principal
Component Analysis of Fuzzy Data Using Autoassociative Neural
Networks", pp336-349, IEEE Transactions on Fuzzy Systems, Vol. 12,
No. 3, June 2004.
[32] P. Kiatgajorm, W. Khan-ngern and S.Nitta ,"The Comparison of Electric
Field Effect Intensity Affects to the Bean Sprouts Growing ", publish by
"Asia -Pacific Conference on Environmenttal Electromagneties
CEEM-2003",Nov.4-7,2003, Hangzhou, China.
[1] Q. Song and B. S. Chissom, "Forecasting enrollments with fuzzy time
series part I", Fuzzy Sets and Systems 54(1993) 1 - 9.
[2] J. Sullivan and William H. Woodall, "A Comparison of Fuzzy
Forecasting and Markov Modelling", Fuzzy Sets and Systems 64(1994)
279 - 293.
[3] Q. Song and B. S. Chissom, "Fuzzy Time Series and its Models", Fuzzy
Sets and Systems 54(1993) 269-277.
[4] H. Bintley, "Time Series analysis with Reveal", Fuzzy Sets and Systems
23(1987) 97-118.
[5] Q. Song and B. S. Chissom, "Forecasting enrollments with fuzzy time
series - part II", Fuzzy Sets and Systems 62(1994) 1-8.
[6] G. A. Tagliarini, J. F. Christ, E. W. Page, "Optimization using Neural
Networks, IEEE Transactions on Computers. vol. 40. no 12. December
ÔÇÿ91 1347-1358
[7] T. K. Bhattacharya and T. K. Basu, "Medium range forecasting of power
system load using modified Kalman filter and Walsch transform",
Electric Power and Energy Systems", vol. 15, no 2, 109 -115, 1993.
[8] F. G. Donaldson and M. Kamstra, "Forecast combining with Neural
Networks", Journal of Forecasting 15(1996) 49-61.
[9] J. V. Hansen and Ray D. Nelson, "Neural Networks and Traditional
Time Series Methods: A Synergistic combination in state Economic
Forecasts", IEEE Transactions on Neural Networks vol. 8, no 4, July
1997.
[10] S. F. Brown, A. J. Branford, W. Moran, "On the use of Artificial Neural
Networks for the analysis of Survival Data", IEEE Transactions on
Neural Networks, vol. 8, no 5, Sept. 1997.
[11] M. Sugeno and K. Tanaka, "Successive Identification of a Fuzzy Model
and its applications to prediction of a complex system", Fuzzy Sets and
Systems 42(1991) 315-334.
[12] L. Zuoyong, Chen Zhenpei, Li Jitao, "A model of Weather Forecast by
Fuzzy Grade Statistics", Fuzzy Sets and Systems 26(1998) 275-281.
[13] L. feng and Xu Xia Guang, "A Forecasting Model of Fuzzy Self
Regression", Fuzzy Sets and Systems 58(1993) 239-242.
[14] M. Ishikawa, T. Moriyama, "Prediction of Time Series by a Structural
Learning of Neural Networks", Fuzzy Sets and Systems 82(1996) 167-
176.
[15] D.H.Goldberg, "Genetic Algorithms in Search, Optimization and
Machine Learning", Addison Wesley Inc, 1999.
[16] J. Paul Choudhury, Dr. Bijan Sarkar and Prof. S. K. Mukherjee, "Some
Issues in building a Fuzzy Neural Network based Framework for
forecasting Engineering Manpower", Proceedings of 34th Annual
Convention of Computer Society of India, Mumbai, pp. 213-227,
October -November 1999.
[17] J. Paul Choudhury, Dr. Bijan Sarkar and Prof. S. K. Mukherjee, "Rule
Base of a Fuzzy Expert Selection System", Proceedings of 34th Annual
Convention of Computer Society of India, Mumbai, pp. 98-104, October
-November 1999.
[18] J. Paul Choudhury, Dr. Bijan Sarkar and Prof. S. K. Mukherjee, "A
Fuzzy Time Series based Framework in the Forecasting Engineering
Manpower in comparison to Markov Modeling", Proceedings of
Seminar on Information Technology, The Institution of
Engineers(India), Computer Engineering Division, West Bengal State
Center, Calcutta, pp. 39-45, March 2000.
[19] G. P. Bansal, A. Jain, A. K. Tiwari and P. K. Chanda, "Optimization in
the operation of Process Plant through Genetic Programming", IETE
Journal of Research, vol 46, no 4, July-August 2000, pp 251-260.
[20] K. K. Shukla, Neuro-genetic prediction of Software Development
Effort", Information and Software Technology 42(2000), pp 701-713.
[21] S. Bandyopadhaya and U. Maulik, "An Improved Evolutionary
Algorithm as Function Optimizer", IETE Journal of Research, vol 46, no
1 and 2, pp 47-56, 2000.
[22] B. Banerjee, A. Konar and S. Mukhopadhayay, " A Neuro-G.A approach
for the Navigational Planning of a Mobile Robot", Proceedings of
International Conference on Communication, Computers and
Devices(ICCD-2000), Department of Electronics and Electrical
Engineering, Indian Institute of Technology, Kharagpur, December
2000, pp 625-628.
[23] J. Paul Choudhury, Dr. Bijan Sarkar and Prof. S. K. Mukherjee,
"Forecasting using Time Series Model Direct Method in comparison to
Indirect Method", Proceedings of International Conference on
Communication, Computers and Devices(ICCD-2000), Department of
Electronics and Electrical Engineering, Indian Institute of Technology,
Kharagpur, December 2000, pp 655-658.
[24] G. W. Snedecor and W.G.Cochran, "Statistical Methods", eight edition,
East Press, 1994.
[25] Dr. J. Paul Choudhury, Satyendra Nath Mandal, Prof Dilip Dey, Prof. S.
K. Mukherjee, Bayesian Learning versus Neural Learning : towards
prediction of Pod Yield", Proceedings of National Seminar on Recent
advances on Information Technology(RAIT-2007), Department of
Computer Science and Engineering, Indian School of Mines University,
Dhanbad, India , pp 298-313, February 2007.
[26] Dr. J. Paul Choudhury, Satyendra Nath Mandal, Prof. S. K. Mukherjee,
"Shoot Length Growth Prediction of Paddy Plant using Neural Fuzzy
Model", Proceedings of National Conference on Cutting Edge
Technologies in Power Conversion & Industrial Drives PCID,
Department of Electrical and Electronics Engineering , Bannari Amman
Institute of Technology.
[27] J. Paul Choudhury, Satyendra Nath Mandal, Prof Dilip Dey, Prof. S. K.
Mukherjee, "Soft Computing model: towords the forecasting of pod
yield as compared to Statistical Model", International Conference on
Information Technology , Haldia,March 2007.
[28] Satyendra Nath Mandal, J. Paul Choudhury, Prof Dilip Dey, Prof. S. R .
Bhadra Chaudhuri ," Growth Estimation With Artificial Neural
Network Considering Weather parameters Using factor and Principal
Component Analysis", CD Proceeding , 10th International Conference on
Information Technology,17-20 December, 2007,Rourkela,India.
[29] Satyendra Nath Mandal, J. Paul Choudhury, Prof Dilip Dey, Prof. S. R .
Bhadra Chaudhuri ," Growth Estimation With simulated Annealing
Considering Weather parameters Using factor and Principal Component
Analysis", Proceeding National Conference on Methods and Models in
Computing (NCM2C 2007),pp184-197,JNU, New Delhi, India.
[30] Satyendra Nath Mandal, J. Paul Choudhury, Prof Dilip Dey, Prof. S. R.
Bhadra Chaudhuri ," A Frame Work for the Development of a Suitable
Method to Find Shoot Length at Maturity of Mustard Plant Using Soft
Computing Model", publish in "International Journal of Computer
Science & Engg.", Vol2. No.3, 2008.
[31] Thierry Denoeux, Member, IEEE, and Marie-Hélène Masson,"Principal
Component Analysis of Fuzzy Data Using Autoassociative Neural
Networks", pp336-349, IEEE Transactions on Fuzzy Systems, Vol. 12,
No. 3, June 2004.
[32] P. Kiatgajorm, W. Khan-ngern and S.Nitta ,"The Comparison of Electric
Field Effect Intensity Affects to the Bean Sprouts Growing ", publish by
"Asia -Pacific Conference on Environmenttal Electromagneties
CEEM-2003",Nov.4-7,2003, Hangzhou, China.
@article{"International Journal of Information, Control and Computer Sciences:62292", author = "Satyendra Nath Mandal and J. Pal Choudhury and Dilip De and S. R. Bhadra Chaudhuri", title = "Roll of Membership functions in Fuzzy Logic for Prediction of Shoot Length of Mustard Plant Based on Residual Analysis", abstract = "The selection for plantation of a particular type of
mustard plant depending on its productivity (pod yield) at the stage
of maturity. The growth of mustard plant dependent on some
parameters of that plant, these are shoot length, number of leaves,
number of roots and roots length etc. As the plant is growing, some
leaves may be fall down and some new leaves may come, so it can
not gives the idea to develop the relationship with the seeds weight at
mature stage of that plant. It is not possible to find the number of
roots and root length of mustard plant at growing stage that will be
harmful of this plant as roots goes deeper to deeper inside the land.
Only the value of shoot length which increases in course of time can
be measured at different time instances. Weather parameters are
maximum and minimum humidity, rain fall, maximum and minimum
temperature may effect the growth of the plant. The parameters of
pollution, water, soil, distance and crop management may be
dominant factors of growth of plant and its productivity. Considering
all parameters, the growth of the plant is very uncertain, fuzzy
environment can be considered for the prediction of shoot length at
maturity of the plant. Fuzzification plays a greater role for
fuzzification of data, which is based on certain membership
functions. Here an effort has been made to fuzzify the original data
based on gaussian function, triangular function, s-function,
Trapezoidal and L –function. After that all fuzzified data are
defuzzified to get normal form. Finally the error analysis
(calculation of forecasting error and average error) indicates the
membership function appropriate for fuzzification of data and use to
predict the shoot length at maturity. The result is also verified using
residual (Absolute Residual, Maximum of Absolute Residual, Mean
Absolute Residual, Mean of Mean Absolute Residual, Median of
Absolute Residual and Standard Deviation) analysis.", keywords = "Fuzzification, defuzzification, gaussian function, triangular function, trapezoidal function,s-function, , membership function, residual analysis.", volume = "2", number = "2", pages = "581-7", }
