P. Saratchandran
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
P. Saratchandran has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- A.A. Pashilkar
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, 639798
1 shared publication - Ajith Abraham
Bennett University
1 shared publication - Narasimhan Sundararajan
Department of Aerospace Engineering, Indian Institute of Science, Bengaluru, India
1 shared publication - Ninan Sajeeth Philip
Artificial Intelligence Research and Intelligent Systems (airis4D), Thelliyoor, India
1 shared publication
Latest Publications
- 31 January 2006
- Published by Elsevier in Aerospace Science and Technology
- Vol. 10 (1) , 49-61
A fault-tolerant neural aided controller for aircraft auto-landing
- 31 January 2006
- Published by Elsevier in Aerospace Science and Technology
- Vol. 10 (1) , 49-61
A fault-tolerant neural aided controller for aircraft auto-landing
- Open Access
Modeling Chaotic Behavior of Stock Indices Using Intelligent Paradigms
The use of intelligent systems for stock market predictions has been widely established. In this paper, we investigate how the seemingly chaotic behavior of stock markets could be well represented using several connectionist paradigms and soft computing techniques. To demonstrate the different techniques, we considered Nasdaq-100 index of Nasdaq Stock MarketS and the S&P CNX NIFTY stock index. We analyzed 7 year's Nasdaq 100 main index values and 4 year's NIFTY index values. This paper investigates the development of a reliable and efficient technique to model the seemingly chaotic behavior of stock markets. We considered an artificial neural network trained using Levenberg-Marquardt algorithm, Support Vector Machine (SVM), Takagi-Sugeno neuro-fuzzy model and a Difference Boosting Neural Network (DBNN). This paper briefly explains how the different connectionist paradigms could be formulated using different learning methods and then investigates whether they can provide the required level of performance, which are sufficiently good and robust so as to provide a reliable forecast model for stock market indices. Experiment results reveal that all the connectionist paradigms considered could represent the stock indices behavior very accurately.
Open AccessModeling Chaotic Behavior of Stock Indices Using Intelligent Paradigms
The use of intelligent systems for stock market predictions has been widely established. In this paper, we investigate how the seemingly chaotic behavior of stock markets could be well represented using several connectionist paradigms and soft computing techniques. To demonstrate the different techniques, we considered Nasdaq-100 index of Nasdaq Stock MarketS and the S&P CNX NIFTY stock index. We analyzed 7 year's Nasdaq 100 main index values and 4 year's NIFTY index values. This paper investigates the development of a reliable and efficient technique to model the seemingly chaotic behavior of stock markets. We considered an artificial neural network trained using Levenberg-Marquardt algorithm, Support Vector Machine (SVM), Takagi-Sugeno neuro-fuzzy model and a Difference Boosting Neural Network (DBNN). This paper briefly explains how the different connectionist paradigms could be formulated using different learning methods and then investigates whether they can provide the required level of performance, which are sufficiently good and robust so as to provide a reliable forecast model for stock market indices. Experiment results reveal that all the connectionist paradigms considered could represent the stock indices behavior very accurately.