Motilal Nehru National Institute of Technology Allahabad

About: Motilal Nehru National Institute of Technology Allahabad is a education organization based out in Allahabad, Uttar Pradesh, India. It is known for research contribution in the topics: Control theory & Electric power system. The organization has 2475 authors who have published 5067 publications receiving 61891 citations. The organization is also known as: NIT Allahabad & Motilal Nehru Regional Engineering College.

Improved cognitive information complexity measure: a metric that establishes program comprehension effort

Abstract: Understanding the software system is known as program comprehension and is a cognitive process. This cognitive process is the driving force behind creation of software that is easier to understand i.e. has lower cognitive complexity, because essentially it is the natural intelligence of human brain that describes the comprehensibility of software. The research area carrying out this study is cognitive informatics. This work has developed an improved cognitive information complexity measure (CICM) that is based on the amount of information contained in the software and encompasses all the major parameters that have a bearing on the difficulty of comprehension or cognitive complexity of software. It is also able to establish the relationship between cognitive complexity of a program and time taken to understand the program, thus mapping closely to the comprehension strategy of a person.

A Nonlinear State Observer Design for 2-DOF Twin Rotor System Using Neural Networks

Abstract: In this paper, a stable neural network based observer for twin rotor multi-input-multi-output (MIMO) system is proposed. The twin rotor MIMO system (TRMS) is a highly nonlinear system. First, a simple local state observer for TRMS is presented. The efficiency of this observer will depend on the accuracy of the model. Then, a neural network – based observer is proposed. This observer can be applied to TRMS system without any a priori knowledge about the system dynamics. A two – layer neural network is used to approximate the nonlinearities of the system. A learning rule for neural network is given which guarantee robustness of the observer. Simulation results are carried out to exemplify the performance of the proposed observers.