The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS
Transactions ID Number: 31-826
Full Name: Sanjay Badjate
Position: Assistant Professor
Age: ON
Sex: Male
Address: Jawaharlal Darda Institute of Engineering and Technology , MIDC Lohara, Yavatmal(M.S)
Country: INDIA
Tel: +919763702571
Tel prefix:
Fax: 07232-249586
E-mail address: s_badjate@rediffmail.com
Other E-mails:
Title of the Paper: Multi step ahead prediction of North and South hemisphere sun spots chaotic time series using focused time lagged recurrent neural network model.
Authors as they appear in the Paper: 1Sanjay L. Badjate 2Sanjay V. Dudul.
Email addresses of all the authors: 1s_badjate@rediffmail.com , 2dudulsv@rediffmail.com
Number of paper pages: 10
Abstract: Multi¨CStep ahead prediction of a chaotic time series is a difficult task that has attracted increasing interest in recent years. The interest in this work is the development of nonlinear neural network models for the purpose of building multi-step chaotic time series prediction. In the literature there is a wide range of different approaches but their success depends on the predicting performance of the individual methods. Also the most popular neural models are based on the statistical and traditional feed forward neural networks. But it is seen that this kind of neural model may present some disadvantages when long-term prediction is required. In this paper focused time lagged recurrent neural network (FTLRNN) model with gamma memory is developed not only for short-term but also for long-term prediction which allows to obtain better predictions of northern and southern chaotic time series in future. The authors experimented the performance of this F!
TLRNN model on predicting the dynamic behavior of typical northern and southern sunspots chaotic time series. Static MLP model is also attempted and compared against the proposed model on the performance measures like mean squared error (MSE), Normalized mean squared error (NMSE) and Correlation Coefficient (r) .The standard back propagation algorithm with momentum term has been used for both the models. The various parameters like number of hidden layers, number of processing elements in the hidden layer, step size, the different learning rules, the various transfer functions like tanh, sigmoid, linear-tanh and linear sigmoid, different error norms L1,L2 (Euclidean), L3, L4 ,L5 and L¡Þ, and different combination of training and testing samples are exhaustively varied and experimented for obtaining the optimal values of performance measures. The obtained results indicates the superior performance of estimated dynamic FTLRNN based model with gamma memory over the stat!
ic MLP NN in various performance metrics. In addition, the output of p
roposed FTLRNN neural network model with gamma memory closely follows the desired output for multi- step ahead prediction for all the chaotic time series considered in the study.
Keywords: Sunspots chaotic time series, multi- step prediction, Focused time lagged neural network (FTLRNN) , Multilayer perceptron (MLP), Self organizing feature map (SOFM).
EXTENSION of the file: .doc
Special (Invited) Session:
Organizer of the Session:
How Did you learn about congress: Milind Labhshetwar , lmilind07@rediffmail.com , Jawaharlal Darda Institute of Engineering and Technology MIDC Lohara Yavatmal
IP ADDRESS: 117.200.192.145
