Particle Swarm Optimization Approach for Multi-step-ahead Prediction Using Radial Basis Function Neural Network

نویسندگان

  • Cezar A. Sierakowski
  • Fábio A. Guerra
  • Leandro dos S. Coelho
چکیده

An alternative approach, between much others, for mathematical representation of dynamics systems with complex or chaotic behaviour, is a radial basis function neural network using k-means for clustering and optimized by pseudo-inverse and particle swarm optimisation. This paper presents the implementation and study to identify a dynamic system, with nonlinear and chaotic behaviour, called Rössler’s circuit, with concepts of multi-step-ahead prediction. Copyright © 2005 IFAC

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Improving Accuracy of DGPS Correction Prediction in Position Domain using Radial Basis Function Neural Network Trained by PSO Algorithm

Differential Global Positioning System (DGPS) provides differential corrections for a GPS receiver in order to improve the navigation solution accuracy. DGPS position signals are accurate, but very slow updates. Improving DGPS corrections prediction accuracy has received considerable attention in past decades. In this research work, the Neural Network (NN) based on the Gaussian Radial Basis Fun...

متن کامل

Multi-step ahead nonlinear identification of Lorenz’s chaotic system using radial basis neural network with learning by clustering and particle swarm optimization

An important problem in engineering is the identification of nonlinear systems, among them radial basis function neural networks (RBF-NN) using Gaussian activation functions models, which have received particular attention due to their potential to approximate nonlinear behavior. Several design methods have been proposed for choosing the centers and spread of Gaussian functions and training the...

متن کامل

Traffic Signal Prediction Using Elman Neural Network and Particle Swarm Optimization

Prediction of traffic is very crucial for its management. Because of human involvement in the generation of this phenomenon, traffic signal is normally accompanied by noise and high levels of non-stationarity. Therefore, traffic signal prediction as one of the important subjects of study has attracted researchers’ interests. In this study, a combinatorial approach is proposed for traffic signal...

متن کامل

Nonlinear Identification Using Neural Network Combined with Training Based on Particle Swarm Optimization

Most processes in industry are characterized by nonlinear and time-varying behavior. In this context, the identification of mathematical models typically nonlinear systems is vital in many fields of engineering. A variety of system identification techniques are applied to the modeling of processes dynamics. Recently, the identification of nonlinear systems by artificial neural networks has been...

متن کامل

Multi-Step-Ahead Prediction of Stock Price Using a New Architecture of Neural Networks

Modelling and forecasting Stock market is a challenging task for economists and engineers since it has a dynamic structure and nonlinear characteristic. This nonlinearity affects the efficiency of the price characteristics. Using an Artificial Neural Network (ANN) is a proper way to model this nonlinearity and it has been used successfully in one-step-ahead and multi-step-ahead prediction of di...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2005