Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

A neural network (NN) for solving the quadratic programming problems (QPPs) in real time by means of steepest descent method (SDM) for problems with inequality constraint is proposed. It is shown that the proposed NN is stable in the sense of Lyapunov and can converge to an exact optimal solution of the original problem. Validity and transient behavior of the proposed NN are illustrated by some simulation results using MATLAB software. © IJSTR 2020.

Department of Mathematics

School of Advanced Sciences

Vellore Campus

World University root node

International Journal of Scientific and Technology Research

Steepest decent method to solve quadratic programming problems with inequality constraints: Neural network

In this Paper, we present a neural network for solving the quadratic programming problems in real time by means of augmented Lagrange multiplier method for problems in standard form. It is shown that the proposed neural network is stable in the sense of Lyapunov and can converge to an exact optimal solution of the original problem. Validity and transient behavior of the proposed neural network are demonstrated by some simulation results using MATLAB software. © RJPT All right reserved.

Research Journal of Pharmacy and Technology

Augmented lagrange multiplier method to solve quadratic programming problems in standard form: A neural network approach

Brause (2001) in his interesting study brought to limelight that human beings,however experienced and however enlightened would go wrong in diagnosing disease. The success percentage of his study are as follows: • Best human diagnosis (most experienced Physicians): 79.7%. • Computer with expert data base: 82.2%. • Computer with 600 patient data: 91.1%. This compels to establish the truth that humans cannot ad hoc analyze error-free complex data. Researchers have found that neural network capabilities can help them to improvise this domain. The implementation of human intelligence in scientific equipment has had been the subject of scientific research for a long time and of the medical research in the last decade. This paper carried out to generate and evaluate both statistical and neural network models to predict malignancy of breast tumor,using Wisconsin Diagnosis Breast Cancer Database (WDBC). The objectives in this article are: (i) Compare the diagnostic performance of statistical and neural network models in distinction between malignance and benign patterns,(ii) Reduce the number of benign cases sent for biopsy using the best model as a supportive tool,and (iii) Validate the capability of the best model to recognize new cases. © 2016,International Journal of Pharmacy and Technology. All rights reserved.

International Journal of Pharmacy and Technology

Canonical discriminant analysis of statistical model and learning vector quantization technique of neural network: A comparative study in diagnosing breast cancer

In this Paper,a conjugate gradient-based neural network is constructed to solve systems of simultaneous linear algebraic equations. The simulation results show that the proposed neural network is feasible and efficient for exactly determined,underdetermined and over-determined systems of linear equations. MATLAB software is used to implement the network in the computer and MATLAB coding program for the neural network has been furnished. © 2016,International Journal of Pharmacy and Technology. All rights reserved.