Heritage Institute of Technology

About: Heritage Institute of Technology is a based out in . It is known for research contribution in the topics: Support vector machine & Transconductance. The organization has 581 authors who have published 1045 publications receiving 8345 citations.

Designing Of An Imporved MRAC With Fuzzy-PD Feedback For Marginally Stable Processes

Abstract: Controlling of time varying processes is not an easy task for conventional controllers with fixed tuning. This limitation can be overcome by employing adaptive controllers as they can modify their control policy according to the changes in process dynamics. In model reference adaptive control (MRAC), controller output depends on the difference between the current process output and the reference model output with a fixed adaptation gain. The desired specifications of the process output are provided as a reference process model. This paper deals with a fuzzy PD feedback based MRAC technique (MRAC-FPD designed for marginally stable processes with dead-time. Improved set-point tracking with smaller overshoot and enhanced load rejection recovery are ensured by the proposed MRAC-FPD controller. Superiority of the proposed MRAC-FPD over conventional MRAC is substantiated through calculated values of performance indices.

On invex programming problem in Hilbert spaces

Abstract: In this paper we introduce the invex programming problem in Hilbert space. The requisite theory has been established to characterize the solution of such class of problems.

Effective GIS Mobile Query System

Abstract: In this paper two new algorithms for GIS Mobile query (point-in-area function) are proposed. This algorithm search the status of a given co-ordinate point with respect to an area (described as a set of co-ordinates without any geometric relationship). In these algorithms computational complexity are reduced. One of the algorithms is much closed to the accuracy of exhaustive search.

GA-based optimal feature weight and parameter selection of NPPC for tea quality estimation

Abstract: Electronic nose (e-nose) is an artificial olfaction system that is being widely used in many industries. E-noses detect smells with the help of electronic signals produced by a number of sensors. The important part of an efficient e-nose system is to recognize these electronic signals accurately by some pattern classification algorithm. Recently developed nonparallel plane proximal classifier (NPPC) has shown its effectiveness in pattern classification task using kernel trick. In general the performance of such classifier depends on the values of optimal parameter set as well as the feature set. In this research work we have studied the effect of simultaneous parameter and feature weight selection on the accuracy of black tea quality estimation employing multiclass one vs. one NPPC. In order to choose the model parameters we have used genetic algorithm (GA). Experimental results show that GA-based tuning and feature weighting scheme increases the performance of NPPC by ∼ 2% in the problem of black tea quality prediction.