Sunspot series prediction using adaptive identification
نویسندگان
چکیده
In this paper a parallel and adaptive methodology for optimizing the time series prediction using System Identification is shown. In order to validate this methodology, a set of time series based on the sun activity measured during the 20th century have been used. The prediction precision for short and long term improves with this technique when it is compared with the found results using System Identification with classical values for the main parameters.
منابع مشابه
Adaptive neural activation functions in multiresolution learning
In this paper, we extend our original work on multiresolution learning, and present a new concept and method of adaptive neural activation functions in multiresolution learning, to maximize the learning efficacy of multiresolution learning paradigm for neural networks. Real-world sunspot series (yearly sunspot data from 1700 to 1999) prediction has been used to evaluate our method. We demonstra...
متن کاملSunspot Forecasting by Using Chaotic Time-series Analysis and NARX Network
Chaotic time-series is a dynamic nonlinear system whose features can not be fully reflected by Linear Regression Model or Static Neural Network. While Nonlinear Autoregressive with eXogenous input includes feedback of network output, therefore, it can better reflect the system’s dynamic feature. Take annual active times of sunspot as an example, after verifying the chaos of sunspot time-series ...
متن کاملModel Based Method for Determining the Minimum Embedding Dimension from Solar Activity Chaotic Time Series
Predicting future behavior of chaotic time series system is a challenging area in the literature of nonlinear systems. The prediction's accuracy of chaotic time series is extremely dependent on the model and the learning algorithm. On the other hand the cyclic solar activity as one of the natural chaotic systems has significant effects on earth, climate, satellites and space missions. Several m...
متن کاملNonlinear Time Series Prediction Based on Lyapunov Theory-Based Fuzzy Neural Network and Multiobjective Genetic Algorithm
This paper presents the nonlinear time series prediction using Lyapunov theory-based fuzzy neural network and multi-objective genetic algorithm (MOGA). The architecture employs fuzzy neural network (FNN) structure and the tuning of the parameters of FNN using the combination of the MOGA and the modified Lyapunov theory-based adaptive filtering algorithm (LAF). The proposed scheme has been used ...
متن کاملOn the Sunspot Time Series Prediction Using Jordon Elman Artificial Neural Network (ann)
In this paper, multi step ahead prediction of monthly sunspot real time series are carried out. This series is highly chaotic in nature [7]. This paper compares performance of proposed Jordan Elman Neural Network with TLRNN (Time lag recurrent neural network), and RNN (Recurrent neural network) for multi-step ahead (1, 6, 12, 18, 24) predictions. It is seen that the proposed neural network mode...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2005