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Govind Ballabh Pant University of Agriculture and Technology, Pantnagar

After independence, development of the rural sector was considered the primary concern of the Government of India. In 1949, with the appointment of the Radhakrishnan University Education Commission, imparting of agricultural education through the setting up of rural universities became the focal point. Later, in 1954 an Indo-American team led by Dr. K.R. Damle, the Vice-President of ICAR, was constituted that arrived at the idea of establishing a Rural University on the land-grant pattern of USA. As a consequence a contract between the Government of India, the Technical Cooperation Mission and some land-grant universities of USA, was signed to promote agricultural education in the country. The US universities included the universities of Tennessee, the Ohio State University, the Kansas State University, The University of Illinois, the Pennsylvania State University and the University of Missouri. The task of assisting Uttar Pradesh in establishing an agricultural university was assigned to the University of Illinois which signed a contract in 1959 to establish an agricultural University in the State. Dean, H.W. Hannah, of the University of Illinois prepared a blueprint for a Rural University to be set up at the Tarai State Farm in the district Nainital, UP. In the initial stage the University of Illinois also offered the services of its scientists and teachers. Thus, in 1960, the first agricultural university of India, UP Agricultural University, came into being by an Act of legislation, UP Act XI-V of 1958. The Act was later amended under UP Universities Re-enactment and Amendment Act 1972 and the University was rechristened as Govind Ballabh Pant University of Agriculture and Technology keeping in view the contributions of Pt. Govind Ballabh Pant, the then Chief Minister of UP. The University was dedicated to the Nation by the first Prime Minister of India Pt Jawaharlal Nehru on 17 November 1960. The G.B. Pant University is a symbol of successful partnership between India and the United States. The establishment of this university brought about a revolution in agricultural education, research and extension. It paved the way for setting up of 31 other agricultural universities in the country.

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Subject: Mathematics × End date: 2013 × Start date: 2013 ×
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    ThesisItemOpen Access
    Study of some probabilistic and computational methods for fuzzy and intuitionistic fuzzy time series forecasting
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2013-08) Gangwar, Sukhdev Singh; Sanjay Kumar
    Time series forecasting in the environment of fuzzy and intuitionistic finds its scope in various branches of sciences and engineering. The present research work mainly focus on development and implementation of fuzzy and intuitionistic fuzzy time series forecasting models. In this study, 6 different models of fuzzy and intuitionistic fuzzy time series forecasting were developed. Model 1, model 2 and model 3 were computational algorithm based higher order fuzzy time series forecasting models. These models were based on multiple partitions which were created using a ratio formula that uses maximum and minimum of time series data. Model 4, model 5 and model 6 used the concept of IFS to include degree of hesitation in time series forecasting. In model 4, the concept of IFS was used in fuzzification process. In model 5, fuzzy sets induced from IFS were used to establish fuzzy logical relations. Model 6 is purely intuitionistic fuzzy time series forecasting model. Both model 4 and model 5 used max-min composition of fuzzy logical relations and model 6 used max-min composition of intuitionistic fuzzy logical relations. All developed models (model 1 to model 6) were implemented on the time series data of enrollments of University of Alabama and share prices of SBI. Results of all forecasting models have been presented graphically to show their trend with actual time series data. Performance analysis of all models has been discussed in terms of MSE and AFE. Time series data of enrollments of University of Alabama were used to show robustness of all models to show their sensitivity towards the unexpected fluctuation in time series data. In forecasting enrollments of University of Alabama, the performance of model 3 in terms of both MSE and AFE is found better than model 1 and model 2. In forecasting share prices of SBI, the performance of model 1 in terms of both MSE and AFE is found better than model 2 and model 3. In forecasting enrollments of University of Alabama, the performance of model 5 in terms of both MSE and AFE is better than other IFS based forecasting models (model 4 and model 6). In forecasting share prices of SBI, the performance of model 6 in terms of MSE is found better than model 4 and model 5 but the performance of model 5 in terms of AFE is found better other model 4 and model 6. Overall, in the case of forecasting the enrollments of University of Alabama, model 3 outperforms than the model 1, model 2, model 4, model 5 and model 6 in terms of MSE and AFE. In terms of MSE and AFE, model 1 outperforms than the model 2, model 3, model 4, model 5 and model 6 in forecasting share prices of SBI. Model 6, intuitionistic fuzzy time series forecasting model and also gives the good performance with respect to other models. The present study may be helpful in research of many forecasting branches as well as in the fields of fuzzy and intuitionistic fuzzy time series forecasting.