Browsing by Author "S.R. Singh"

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A computational algorithm for the solution of fully fuzzy multi-objective linear programming problem
(Springer Berlin Heidelberg, 2018) S.K. Bharati; Abhishekh; S.R. Singh
The present paper defines a new distance function between two trapezoidal fuzzy (TrF) numbers which satisfies all the properties of metric and a degree of deviation between two TrF numbers. The proposed degree of deviation has been used to solve a fully fuzzy multi-objective linear programming problem (FMOLPP). The proposed algorithm uses converting a FMOLPP into crisp linear programming problem and then to get Pareto-optimal solution to the problem. Further, with Pareto-optimal solutions a balance Pareto optimal solution of the problem using fuzzy optimization technique has been obtained with minimum deviation degree in decision variables. The developed computational algorithm has been implemented on an example for the validity of the proposed algorithm. © 2017, Springer-Verlag GmbH Germany.
A computational method of forecasting based on fuzzy time series
(2008) S.R. Singh
In this paper, a computational method of forecasting based on fuzzy time series have been developed to provide improved forecasting results to cope up the situation containing higher uncertainty due to large fluctuations in consecutive year's values in the time series data and having no visualization of trend or periodicity. The proposed model is of order three and uses a time variant difference parameter on current state to forecast the next state. The developed model has been tested on the historical student enrollments, University of Alabama to have comparison with the existing methods and has been implemented for forecasting of a crop production system of lahi crop, containing higher uncertainty. The suitability of the developed model has been examined in comparison with the other models to show its superiority. © 2008 IMACS.
A modified weighted method of time series forecasting in intuitionistic fuzzy environment
(Springer, 2020) Surendra Singh Gautam; Abhishekh; S.R. Singh
In this paper, we present a modified weighted method of time series forecasting using intuitionistic fuzzy sets. The proposed weighted method provides a better approach to extent of the accuracy in forecasted outputs. As it is established that the length of interval plays a crucial role in forecasting the historical time series data, so a new technique is proposed to define the length of interval and the partition of the universe of discourse into unequal length of intervals. Further, triangular fuzzy sets are defined and obtain membership grades of each datum in historical time series data to their respective triangular fuzzy sets. Based on the score and accuracy function of intuitionistic fuzzy number, the historical time series data is intuitionistic fuzzified and assigned the weight for intuitionistic fuzzy logical relationship groups. Defuzzification technique is based on the defined intuitionistic fuzzy logical relationship groups and provides better forecasting accuracy rate. The proposed method is implemented to forecast the enrollment data at the University of Alabama and market share price of SBI at BSE India. The results obtained have been compared with other existing methods in terms of root mean square error and average forecasting error to show the suitability of the proposed method. © 2020, Operational Research Society of India.
A new centroid method of ranking for intuitionistic fuzzy numbers
(Springer Verlag, 2014) Anil Kumar Nishad; Shailendra Kumar Bharati; S.R. Singh
In this paper, we proposed a new ranking method for intuitionistic fuzzy numbers (IFNs) by using centroid and circumcenter of membership function and non-membership function of the intuitionistic fuzzy number. The method utilizes the midpoint of the circumcenter of membership and non-membership function of intuitionistic fuzzy number to define the ranking function for IFN satisfying the general axioms of ranking functions. The developed method has been illustrated by some examples and is compared with some existing ranking method to show its suitability. © Springer India 2014.
A New High-Order Approach for Forecasting Fuzzy Time Series Data
(World Scientific Publishing Co., 2018) Surendra Singh Gautam; Abhishekh; S.R. Singh
In forecasting the fuzzy time series data, several authors took grades of membership 1, 0.5 and 0 for linguistic interval corresponding to fuzzy set. In this paper, we have proposed high-order approach for forecasting the fuzzy time series data by using the grade of membership value defined for each datum corresponding to triangular fuzzy sets and fuzzify the historical data by triangular fuzzy sets which have their maximum membership values. Also, we establish high-order fuzzy logical relationship groups and give a new technique for defuzzification process, by which we can compute the forecasted value in a more efficient way with lower value of MSE. For verifying the suitability of proposed method, we illustrate time series data of student enrollments at the University of Alabama, USA, and crop (Lahi) production of Pantnagar farm, G. B. Pant University of Agriculture and Technology, Pantnagar, India. The forecasting accuracy rate of proposed high-order forecasting method is better than those of existing methods and the forecasted production is much closer to the actual production. © 2018 World Scientific Publishing Europe Ltd.
A new interval-valued intuitionistic fuzzy numbers: Ranking methodology and application
(World Scientific Publishing Co. Pte Ltd, 2018) S.K. Bharati; S.R. Singh
Ranking of interval-valued intuitionistic fuzzy (IVIF) numbers is a most popular and elegant work in the area of decision-making of several real-world problems. Some limited methods have been presented concerning the ranking of IVIF sets in literature. In the present paper, we generalize the intuitionistic fuzzy (IF) number to interval-valued intuitionistic fuzzy number by defining interval membership and nonmembership functions instead of fixed-valued function and hence it will present uncertain situation better than IF numbers. It may also be applied in data analysis, industrial management, artificial intelligence, forecasting, time series and so on. In this paper, ranking methodology of IVIF numbers is presented, for this first we define the value and ambiguity of IVIF numbers. Proposed ranking method also is compared with existing ranking methods. Further, IVIF numbers are used to capture fuzziness and hesitation in transportation problem (TP), and we propose a new method to find optimal solutions of TP with IVIF number parameters and finally, a numerical example is given to demonstrate the proposed method. © World Scientific Publishing Company.
A new method of time series forecasting using intuitionistic fuzzy set based on average-length
(Taylor and Francis Ltd., 2020) Abhishekh; Surendra Singh Gautam; S.R. Singh
The major problem in the field of fuzzy time series (FTS) is the accuracy rate in the forecasted values. To overcome this problem here, we propose a model for intuitionistic FTS forecasting based on average-length of interval, which enhances the forecasting result. The proposed model is focused on how to fuzzify the historical time series data. Here, the fuzzification of each observation is intuitionistic fuzzification, which is based on the maximum degree of score function and also establishes intuitionistic fuzzy logical relationships (IFLR) among all intuitionistic fuzzified data set. Here, we use simple arithmetic computations in defuzzification process with measuring the frequency of IFLR. An illustrative example of enrollments at the University of Alabama is used to verify the effectiveness of the proposed model and comparison in terms of RMSE and AFE with some of the existing forecasting models to show its superiority. © 2020, © 2020 Chinese Institute of Industrial Engineers.
A novel approach to handle forecasting problems based on moving average two-factor fuzzy time series
(Springer Verlag, 2019) Abhishekh; S.K. Bharati; S.R. Singh
In this paper, we present a novel approach to handling forecasting problems based on moving average in two-factor fuzzy time series. The proposed method defines a new technique to partition the universe of discourse into number of intervals based on the number of observations available in the historical time series data. Partition of interval depends on the transformed moving average time series data rather than actual time series data sets. Further, triangular fuzzy set is defined for transformed moving average data set and obtained membership grades of each moving average datum to their corresponding triangular fuzzy sets. Also, variation data set is calculated from transformed moving average data sets to define second factor data set. Further, frequency occurrence of fuzzy logical relationships is used in defuzzification process. The proposed method of moving average forecasting is verified and certified with three different fuzzy time series models. The robustness of proposed method is implemented in forecasting of Bombay Stock Exchange (BSE) Sensex historical data and compared in terms of different statistical error which indicates that the proposed method can provide more accurate forecasted values over with existing fuzzy time series models. © Springer Nature Singapore Pte Ltd. 2019.
A refined method of forecasting based on high-order intuitionistic fuzzy time series data
(Springer Verlag, 2018) Abhishekh; Surendra Singh Gautam; S.R. Singh
In this paper, we present a refined method of forecasting based on high-order intuitionistic fuzzy time series by transformed a historical fuzzy time series data into intuitionistic fuzzy time series data via defining their appropriate membership and non-membership function. The fuzzification of historical time series data is intuitionistic fuzzification which is based on their score and accuracy function. Also intuitionistic fuzzy logical relationship groups are defined and introduced a defuzzification process for high-order intuitionistic fuzzy time series. The aim of this paper is to propose an idea of high-order intuitionistic fuzzy time series which is generalization of fuzzy time series models and its experimental result shows that the proposed high-order intuitionistic fuzzy forecasting method gets better forecasting accuracy rates over the existing methods. The proposed method has been implemented on the historical enrollment data at the University of Alabama. The comparison result of these illustration shows that the proposed method has smaller forecasting accuracy rates in terms of MSE and MAPE over than the existing models in fuzzy time series. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.
A refined weighted method for forecasting based on type 2 fuzzy time series
(Taylor and Francis Ltd., 2018) Abhishekh; Surendra Singh Gautam; S.R. Singh
In this paper, we proposed a method for type 2 fuzzy time series forecasting which is an extension of type 1 fuzzy time series model to enhance the accuracy in forecasts. The proposed method uses frequency distribution approach to define the appropriate length of intervals. High and low observations are used to define type 2 fuzzy time series and different fuzzy logical relationship groups (FLRGs) have been obtained for both high and low observations. Further, weight function are defined with the help of FLRGs to compute forecasted outputs by a simple arithmetic mean rather than complicated union and intersection operator of type 2 fuzzy sets. The proposed method has been applied for forecasting university enrollments and crop (wheat) production. It is shown that the proposed method has higher accuracy in terms of mean absolute percent error and root-mean-square error (RMSE) as compared to the other fuzzy time series methods. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.
A robust method of forecasting based on fuzzy time series
(2007) S.R. Singh
Present study proposes an improved and versatile method of forecasting based on the concept fuzzy time series forecasting. The developed model has been presented in a form of simple computational algorithms. It utilizes various difference parameters being implemented on current state for forecasting the next state values to accommodate the possible vagueness in the data in a better way and making it a robust method. The developed model has been implemented on the historical student enrollments data of University of Alabama (adapted by Song and Chissom) and the obtained forecasted values have been compared with the existing methods to show its superiority. The robustness of the model has also been tested in comparison. The suitability of the developed model has also been examined in the crop production forecasting by implementing it on historical time series data of rice production of Pantnagar(Farm), India. © 2006 Elsevier Inc. All rights reserved.
A Score Function-Based Method of Forecasting Using Intuitionistic Fuzzy Time Series
(World Scientific Publishing Co. Pte Ltd, 2018) Abhishekh; Surendra Singh Gautam; S.R. Singh
Intuitionistic fuzzy set plays a vital role in data analysis and decision-making problems. In this paper, we propose an enhanced and versatile method of forecasting using the concept of intuitionistic fuzzy time series (FTS) based on their score function. The developed method has been presented in the form of simple computational steps of forecasting instead of complicated max-min compositions operator of intuitionistic fuzzy sets to compute the relational matrix R. Also, the proposed method is based on the maximum score and minimum accuracy function of intuitionistic fuzzy numbers (IFNs) to fuzzify the historical time series data. Further intuitionistic fuzzy logical relationship groups are defined and also provide a forecasted value and lies in an interval and is more appropriate rather than a crisp value. Furthermore, the proposed method has been implemented on the historical student enrollments data of University of Alabama and obtains the forecasted values which have been compared with the existing methods to show its superiority. The suitability of the proposed model has also been examined to forecast the movement of share market price of State Bank of India (SBI) at Bombay Stock Exchange (BSE). The results of the comparison of MSE and MAPE indicate that the proposed method produces more accurate forecasting results. © 2018 World Scientific Publishing Company.
A simple method of forecasting based on fuzzy time series
(2007) S.R. Singh
In fuzzy time series forecasting various methods have been developed to establish the fuzzy relations on time series data having linguistic values for forecasting the future values. However, the major problem in fuzzy time series forecasting is the accuracy in the forecasted values. The present paper proposes a new method of fuzzy time series forecasting based on difference parameters. The proposed method is a simplified computational approach for the forecasting. The method has been implemented on the historical enrollment data of University of Alabama (adapted by Song and Chissom) and the forecasted values have been compared with the results of the existing methods to show is superiority. Further, the proposed method has also been implemented on a real life problem of crop production forecast of wheat crop and the results have been compared with other methods. © 2006 Elsevier Inc. All rights reserved.
A statistical theory for the decay process of weak homogeneous convective turbulence
(Kluwer Academic Publishers, 1985) N. Kishore; S.R. Singh
In this paper we have derived kinetic equations for the decay of kinetic and thermal energy of a weak homogenous turbulent flow in which the fluctuating temperature field is superimposed on the eddy velocity field. Random fluctuations of velocity and temperature in a one-dimensional model have been considered on the basis of wavenumbers in Fourier space together with linearized mode approximations. Energy decay equations have been obtained in closed form, using quasi-normal approximations and the Bogoliubov expansion method. The paper also discusses the cases of f=v and f=0. © 1985 D. Reidel Publishing Company.
An intutive solution of a convexity problem
(2011) Triloki Nath; S.R. Singh
[No abstract available]
Boundedness of certain sets of Lagrange multipliers in vector optimization
(Elsevier Inc., 2015) Triloki Nath; S.R. Singh
In this paper, we establish Lagrange multiplier rules in terms of Michel-Penot subdifferential for nonsmooth vector optimization problem. A constraint qualification or regularity condition in terms of Michel-Penot subdifferential is given and under this regularity condition the boundedness of certain sets of Lagrange multipliers are discussed. © 2015 Elsevier Inc.Allrightsreserved.
Effect of age of seedling and spacing on yield, economics, soil health and digestibility of rice (Oryza sativa) genotypes under system of rice intensification
(2013) Kalyan Singh; S.R. Singh; J.K. Singh; R.S. Rathore; Shishu Pal Singh; Rina Roy
A field experiment was conducted at the Institute of Agricultural Sciences, Banaras Hindu University, Varanasi during 2008 to 2010 to evaluate the influence of system of rice intensification (SRI) under different age of seedling and spacing on productivity of rice genotypes and soil health. Rice genotype PHB 71 was significantly superior to NDR 359 with respect to yield attributes, grain yield, economics, nutrient uptake and soil health. Ten days old seedlings were also significantly better than those of other age of 12 and 14 days old seedlings in respect of yield attributes, grain yield, economics, nutrient uptake and soil health. Similarly wider row spacing of 30 cm × 30 cm significantly favoured higher values of yield attributes, grain yield, economics, nutrient uptake, and soil health due to profuse root growth. Rice hybrid PHB 71 had poor digestibility of rice straw having higher values of crude protein content, crude fiber, oxalic acid and lower values of nitrogen free extract and total ash compared to NDR 359.
Effect of anisotropy and viscous dissipation on turbulence production
(Kluwer Academic Publishers, 1985) N. Kishore; S.R. Singh
The paper is concerned with turbulent flow of incompressible, spatially homogeneous viscous fluid. A model for turbulence energy equation is obtained, ignoring the pressure redistribution term in dynamical equations for the Reynolds stresses. The mechanism of dissipation on turbulence production is discussed and shown that the turbulence kinetic energy decays upto a constant value as time becomes infinitely large, i.e., for isotropy, dissipation inhibits the production process and if {Mathematical expression} > {Mathematical expression} initially then dissipation causes reduction in anisotropy. © 1985 D. Reidel Publishing Company.
Effect of tillage, inter-terrace and mulching on yield and nutrient uptake by maize (Zea mays)-chickpea (Cicer arietinum) cropping system
(2002) Kalyan Singh; U.N. Singh; Y. Singh; S.R. Singh; R.S. Chandel
A field experiment was conducted during 1997-98 to 1999-2000 at the Research Farm of University situated at Barkachha (Mirzapur) under Vindhyan hills, to study the effect of tillage, inter-terrace and mulching on yield and nutrient uptake by maize (Zea mays L)-chickpea (Cicer arietinum L.) cropping system. Deep tillage was more remunerative than off-season and conventional tillage with respect to maize yield (6.21 tonnes/ha), chickpea yield (1.15 tonnes/ha) and nutrient uptake (262.79 N, 49.84 P, 219.37 K kg/ha) in maize-chickpea cropping system. Earthen bund was superior to live bund as well as live bund + small section bund. Under mulching treatments stover mulch was superior to soil mulch and control (no mulch).
Efficient tillage and nutrient management practices for sustainable yields, profitability and energy use efficiency for rice-based cropping system in different soils and agro-climatic conditions
(2013) G. R. Maruthi Sankar; K.L. Sharma; K. Srinivas Reddy; G. Pratibha; Reshma Shinde; S.R. Singh; A.K. Nema; R.P. Singh; B.S. Rath; A. Mishra; B.D. Behera; C.R. Subudhi; Bhagwan Singh; H.C. Singh; Ashok Kumar Singh; D.K. Rusia; M.S. Yadava; C.R. Thyagaraj; P.K. Mishra; M. Suma Chandrika; B. Venkateswarlu
Long-term tillage and fertilizer experiments were conducted in rice in kharif followed by lentil in dry subhumid Inceptisols at Varanasi and Faizabad; horse gram at Phulbani and linseed at Ranchi in moist subhumid Alfisols in rabi during 2001 to 2010. The study was conducted to assess the effect of conventional tillage (CT), low tillage + interculture (LT1) and low tillage + herbicide (LT2) together with 100% N (organic) (F1), 50% N (organic) + 50% N (inorganic) (F2) and 100% N (inorganic) (F3) on productivity, profitability, rainwater and energy use efficiencies. The results at Varanasi revealed that CT was superior with mean yield of 2389 kg ha-1, while F1 was superior with 2378 kg ha-1 in rice. At Faizabad, CT was superior with mean rice yield of 1851 kg ha-1 and lentil yield of 977 kg ha -1, while F1 was superior with 1704 and 993 kg ha-1 of rice and lentil, respectively. At Phulbani, F2 was superior with rice yield of 1170 kg ha-1. At Ranchi, F2 with rice yield of 986 kg ha-1 and F3 with linseed yield of 224 kg ha-1 were superior. The regression model of crop seasonal rainfall and yield deviations indicated an increasing trend in rice yield over mean (positive deviation) with increase in rainfall at all locations; while a decreasing trend (negative deviation) was found for lentil at Faizabad, horse gram at Phulbani and linseed at Ranchi. Based on economic analysis, CTF1 at Varanasi and Faizabad, CTF2 at Phulbani and LT2F2 at Ranchi were superior. Copyright © 2013 Cambridge University Press.