Mapping Fatou-Julia Iterations

2018-01-01
Akhmet, Marat
Alejaily, Ejaily Milad
A new method of fractals construction based on Fatou-Julia iteration is proposed. We develop a non-ordinary way to map fractal into a new fractal, where the mapping function is involved in the dynamics of the generated formula such that the modification does not violate the Julia and Fatou recipe for creating fractals. The method is applied for both Mandelbrot and Julia sets, and we follow the same technique of determining the fractal set using in the original iteration. The results of this paper are expected to have beneficial effects on many fields such as computer graphics and 3D printing technologies.

Suggestions

Dynamics for chaos and fractals
Alejaily, Ejaily; Akhmet, Marat; Department of Mathematics (2019)
In this thesis, we study how to construct and analyze dynamics for chaos and fractals. After the introductory chapter, we discuss in the second chapter the chaotic behavior of hydrosphere parameters and their influence on global weather and climate. For this purpose, we investigate the nature and source of unpredictability in the dynamics of sea surface temperature. The impact of sea surface temperature variability on the global climate is clear during some global climate patterns like the El Niño-Southern ...
Component extraction analysis of multivariate time series
Akman, I; DeGooijer, JG (1996-05-01)
A method for modelling several observed parallel time series is proposed. The method involves seeking possible common underlying pure AR and MA components in the series. The common components are forced to be mutually uncorrelated so that univariate time series modelling and forecasting techniques can be applied. The proposed method is shown to be a useful addition to the time series analyst's toolkit, if common sources of variation in multivariate data need to be quickly identified.
Compressed images for affinity prediction-2 (CIFAP-2): an improved machine learning methodology on protein-ligand interactions based on a study on caspase 3 inhibitors
Erdas, Ozlem; Andac, Cenk. A.; Gurkan-Alp, A. Selen; Alpaslan, Ferda Nur; Buyukbingol, Erdem (Informa UK Limited, 2015-01-01)
The aim of this study is to propose an improved computational methodology, which is called Compressed Images for Affinity Prediction-2 (CIFAP-2) to predict binding affinities of structurally related protein-ligand complexes. CIFAP-2 method is established based on a protein-ligand model from which computational affinity information is obtained by utilizing 2D electrostatic potential images determined for the binding site of protein-ligand complexes. The quality of the prediction of the CIFAP-2 algorithm was ...
Building Morphological Chains for Agglutinative Languages
Ozen, Serkan; CAN BUĞLALILAR, BURCU (2017-04-23)
In this paper, we build morphological chains for agglutinative languages by using a log linear model for the morphological segmentation task. The model is based on the unsupervised morphological segmentation system called MorphoChains [1]. We extend MorphoChains log linear model by expanding the candidate space recursively to cover more split points for agglutinative languages such as Turkish, whereas in the original model candidates are generated by considering only binary segmentation of each word. The re...
Temporal neuro-fuzzy MAR Algorithm for time series data in rule-based systems
Sisman, NA; Alpaslan, Ferda Nur (1998-04-23)
This paper introduces a new neuro-fuzzy model for constructing a knowledge-base of temporal fuzzy rules obtained by MAR (Multivariate Autoregressive) Algorithm. The model described contains two main parts which are fuzzy-rule extraction and storage of them. The fuzzy rules are obtained from time series data using MAR Algorithm. Fuzzy linear function with fuzzy number coefficients are used. The extracted rules are fed into the temporal fuzzy multilayer feedforward neural network.
Citation Formats
M. Akhmet and E. M. Alejaily, “Mapping Fatou-Julia Iterations,” 2018, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/47259.