A Web-Based Intelligent Educational Laboratory System for Forecasting Chaotic Time Series

2015 ◽  
pp. 110-135
Author(s):  
Utku Kose ◽  
Ahmet Arslan
Keyword(s):  
Artificial Intelligence ◽  
Time Series ◽  
Chaos Theory ◽  
Intelligent Systems ◽  
Chaotic Systems ◽  
Chaotic Time Series ◽  
Laboratory System ◽  
Web Based ◽  
Intelligent Water Drops Algorithm ◽  
Forecast Time

In the context of Chaos Theory and its applications, forecasting time series of a chaotic system is an attractive work area for the current literature. Many different approaches and the related scientific studies have been introduced and done by researchers since the inception of this working area. Newer studies are also performed in order to provide more effective and efficient approaches and improve the related literature in this way. On the other hand, it is another important research point to ensure effective educational approaches for teaching Chaos Theory and chaotic systems within the associated courses. In this sense, this chapter introduces a Web-based, intelligent, educational laboratory system for forecasting chaotic time series. Briefly, the system aims to enable students to experience their own learning process over the Web by using a simple interface. The laboratory system employs an Artificial Intelligence-based approach including a Single Multiplicative Neuron System trained by Intelligent Water Drops Algorithm in order to forecast time series of chaotic systems. It is possible to adjust parameters of the related Artificial Intelligence techniques, so it may possible for students to have some knowledge about Artificial Intelligence and intelligent systems.

scholarly journals Chaotic Systems and Their Recent Implementations on Improving Intelligent Systems

Economics ◽  
2015 ◽  
pp. 1167-1200
Author(s):  
Utku Köse ◽  
Ahmet Arslan
Keyword(s):  
Artificial Intelligence ◽  
Chaos Theory ◽  
Intelligent Systems ◽  
Chaotic Systems ◽  
Nonlinear Dynamical Systems ◽  
Initial Conditions ◽  
Research Effort ◽  
Nonlinear Dynamical ◽  
Main Research ◽  
Reference Guide

Chaos Theory is a kind of a scientific approach/research effort which is based on examining behaviors of nonlinear dynamical systems which are highly sensitive to their initial conditions. Currently, there are many different scientific studies based on the Chaos Theory and the related solution approaches, methods, or techniques for problems of this theory. Additionally, the theory is used for improving the introduced studies of different fields in order to get more effective, efficient, and accurate results. At this point, this chapter aims to provide a review-based study introducing recent implementations of the Chaos Theory on improving intelligent systems, which can be examined in the context of the Artificial Intelligence field. In this sense, the main research way is directed into the works performed or introduced mostly in years between 2008 and 2013. By providing a review-based study, the readers are enabled to have ideas on Chaos Theory, Artificial Intelligence, and the related works that can be examined within intersection of both fields. At this point, the chapter aims to discuss not only recent works, but also express ideas regarding future directions within the related implementations of chaotic systems to improve intelligent systems. The chapter is generally organized as a reference guide for academics, researchers, and scientists tracking the literature of the related fields: Artificial Intelligence and the Chaos Theory.

Chaotic Systems and Their Recent Implementations on Improving Intelligent Systems

2014 ◽  
pp. 69-101 ◽  
Author(s):  
Utku Köse ◽  
Ahmet Arslan
Keyword(s):  
Artificial Intelligence ◽  
Chaos Theory ◽  
Intelligent Systems ◽  
Chaotic Systems ◽  
Nonlinear Dynamical Systems ◽  
Initial Conditions ◽  
Research Effort ◽  
Nonlinear Dynamical ◽  
Main Research ◽  
Reference Guide

Chaos Theory is a kind of a scientific approach/research effort which is based on examining behaviors of nonlinear dynamical systems which are highly sensitive to their initial conditions. Currently, there are many different scientific studies based on the Chaos Theory and the related solution approaches, methods, or techniques for problems of this theory. Additionally, the theory is used for improving the introduced studies of different fields in order to get more effective, efficient, and accurate results. At this point, this chapter aims to provide a review-based study introducing recent implementations of the Chaos Theory on improving intelligent systems, which can be examined in the context of the Artificial Intelligence field. In this sense, the main research way is directed into the works performed or introduced mostly in years between 2008 and 2013. By providing a review-based study, the readers are enabled to have ideas on Chaos Theory, Artificial Intelligence, and the related works that can be examined within intersection of both fields. At this point, the chapter aims to discuss not only recent works, but also express ideas regarding future directions within the related implementations of chaotic systems to improve intelligent systems. The chapter is generally organized as a reference guide for academics, researchers, and scientists tracking the literature of the related fields: Artificial Intelligence and the Chaos Theory.

scholarly journals Local Functional Coefficient Autoregressive Model for Multistep Prediction of Chaotic Time Series

2015 ◽  
Vol 2015 ◽  
pp. 1-13
Author(s):  
Liyun Su ◽  
Chenlong Li
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Chaos Theory ◽  
Nearest Neighbor ◽  
Simulated Data ◽  
Real Data ◽  
Chaotic Time Series ◽  
Dimensional Phase Space ◽  
Local Functional ◽  
Functional Coefficient

A new methodology, which combines nonparametric method based on local functional coefficient autoregressive (LFAR) form with chaos theory and regional method, is proposed for multistep prediction of chaotic time series. The objective of this research study is to improve the performance of long-term forecasting of chaotic time series. To obtain the prediction values of chaotic time series, three steps are involved. Firstly, the original time series is reconstructed inm-dimensional phase space with a time delayτby using chaos theory. Secondly, select the nearest neighbor points by using local method in them-dimensional phase space. Thirdly, we use the nearest neighbor points to get a LFAR model. The proposed model’s parameters are selected by modified generalized cross validation (GCV) criterion. Both simulated data (Lorenz and Mackey-Glass systems) and real data (Sunspot time series) are used to illustrate the performance of the proposed methodology. By detailed investigation and comparing our results with published researches, we find that the LFAR model can effectively fit nonlinear characteristics of chaotic time series by using simple structure and has excellent performance for multistep forecasting.

Local Adaptive Nonlinear Filter Prediction Model with a Parameter for Chaotic Time Series

2010 ◽  
Vol 44-47 ◽  
pp. 3180-3184
Author(s):  
Fen Fang ◽  
Hai Yan Wang ◽  
Zhou Mu Yang
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Gradient Descent ◽  
Chaotic Systems ◽  
Lorenz System ◽  
Logistic Map ◽  
Predictive Performance ◽  
Nonlinear Filter ◽  
Chaotic Time Series ◽  
Proposed Model

In order to improve the predictive performance for chaotic time series， we propose a novel local adaptive nonlinear filter prediction model. We use a function with a parameter to build an adaptive nonlinear filter in this model, and we train this model with an adaptive algorithm, deduced by the minimum square-root-error criterion and the steepest gradient descent rule. We evaluate the proposed model using four well-known chaotic systems, namely Logistic map, Henon map, Lorenz system and Rosslor system. All the results show a remarkable increase in predictive performance, comparing with the local adaptive nonlinear filter prediction model.

scholarly journals Constructing Digitized Chaotic Time Series with a Guaranteed Enhanced Period

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Chuanfu Wang ◽  
Qun Ding
Keyword(s):  
Time Series ◽  
Chaotic Systems ◽  
Chaotic Behavior ◽  
Logistic Map ◽  
Approximate Entropy ◽  
Chaotic Time Series ◽  
Pseudorandom Sequence ◽  
Periodic Behavior ◽  
Experimental Implementation ◽  
Sequence Generator

When chaotic systems are realized in digital circuits, their chaotic behavior will degenerate into short periodic behavior. Short periodic behavior brings hidden dangers to the application of digitized chaotic systems. In this paper, an approach based on the introduction of additional parameters to counteract the short periodic behavior of digitized chaotic time series is discussed. We analyze the ways that perturbation sources are introduced in parameters and variables and prove that the period of digitized chaotic time series generated by a digitized logistic map is improved efficiently. Furthermore, experimental implementation shows that the digitized chaotic time series has great complexity, approximate entropy, and randomness, and the perturbed digitized logistic map can be used as a secure pseudorandom sequence generator for information encryption.

scholarly journals Wavelet characterization of hyper-chaotic time series

2018 ◽  
Vol 64 (3) ◽  
pp. 283 ◽  
Author(s):  
J. S. Murguía ◽  
H. C. Rosu ◽  
L. E. Reyes-López ◽  
M. Mejía-Carlos ◽  
C. Vargas-Olmos
Keyword(s):  
Time Series ◽  
Chaotic Systems ◽  
Chaotic Time Series ◽  
Wavelet Coefficients ◽  
Numerical Characterization

A wavelet scaling numerical characterization of time series based on the variance of the wavelet coefficients is used for three well-known four-dimensional and one five-dimensional hyper-chaotic systems. We report several scaling behaviors for the states of these hyper-chaotic systems.

Artificial intelligence as a moving force of improvement and innovative development in education and pedagogic

2019 ◽  
pp. 21-30
Author(s):  
M. G. Koliada ◽  
T. I. Bugayova
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Systems ◽  
Intelligent System ◽  
Interaction Analysis ◽  
Educational Data Mining ◽  
Main Task ◽  
Intellectual System ◽  
Will Force ◽  
To Come ◽  
Artificial Intelligence Systems

The article discusses the history of the development of the problem of using artificial intelligence systems in education and pedagogic. Two directions of its development are shown: “Computational Pedagogic” and “Educational Data Mining”, in which poorly studied aspects of the internal mechanisms of functioning of artificial intelligence systems in this field of activity are revealed. The main task is a problem of interface of a kernel of the system with blocks of pedagogical and thematic databases, as well as with the blocks of pedagogical diagnostics of a student and a teacher. The role of the pedagogical diagnosis as evident reflection of the complex influence of factors and reasons is shown. It provides the intelligent system with operative and reliable information on how various reasons intertwine in the interaction, which of them are dangerous at present, where recession of characteristics of efficiency is planned. All components of the teaching and educational system are subject to diagnosis; without it, it is impossible to own any pedagogical situation optimum. The means in obtaining information about students, as well as the “mechanisms” of work of intelligent systems based on innovative ideas of advanced pedagogical experience in diagnostics of the professionalism of a teacher, are considered. Ways of realization of skill of the teacher on the basis of the ideas developed by the American scientists are shown. Among them, the approaches of researchers D. Rajonz and U. Bronfenbrenner who put at the forefront the teacher’s attitude towards students, their views, intellectual and emotional characteristics are allocated. An assessment of the teacher’s work according to N. Flanders’s system, in the form of the so-called “The Interaction Analysis”, through the mechanism of fixing such elements as: the verbal behavior of the teacher, events at the lesson and their sequence is also proposed. A system for assessing the professionalism of a teacher according to B. O. Smith and M. O. Meux is examined — through the study of the logic of teaching, using logical operations at the lesson. Samples of forms of external communication of the intellectual system with the learning environment are given. It is indicated that the conclusion of the found productive solutions can have the most acceptable and comfortable form both for students and for the teacher in the form of three approaches. The first shows that artificial intelligence in this area can be represented in the form of robotized being in the shape of a person; the second indicates that it is enough to confine oneself only to specially organized input-output systems for targeted transmission of effective methodological recommendations and instructions to both students and teachers; the third demonstrates that life will force one to come up with completely new hybrid forms of interaction between both sides in the form of interactive educational environments, to some extent resembling the educational spaces of virtual reality.

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