Classification of KPZQ and BDP models by multiaffine analysis
نویسندگان
چکیده
We argue differences between the Kardar-Parisi-Zhang with Quenched disorder (KPZQ) and the Ballistic Deposition with Power-law noise (BDP) models, using the multiaffine analysis method. The KPZQ and the BDP models show mono-affinity and multiaffinity, respectively. This difference results from the different distribution types of neighbor-height differences in growth paths. Exponential and power-law distributions are observed in the KPZQ and the BDP, respectively. In addition, we point out the difference of profiles directly, i.e., although the surface profiles of both models and the growth path of the BDP model are rough, the growth path of the KPZQ model is smooth.
منابع مشابه
Interval network data envelopment analysis model for classification of investment companies in the presence of uncertain data
The main purpose of this paper is to propose an approach for performance measurement, classification and ranking the investment companies (ICs) by considering internal structure and uncertainty. In order to reach this goal, the interval network data envelopment analysis (INDEA) models are extended. This model is capable to model two-stage efficiency with intermediate measures i...
متن کاملLinear and Nonlinear Multivariate Classification of Iranian Bottled Mineral Waters According to Their Elemental Content Determined by ICP-OES
The combinations of inductively coupled plasma-optical emission spectrometry (ICP-OES) and three classification algorithms, i.e., partial least squares discriminant analysis (PLS-DA), least squares support vector machine (LS-SVM) and soft independent modeling of class analogies (SIMCA), for discriminating different brands of Iranian bottled mineral waters, were explored. ICP-OES was used for th...
متن کاملSparse Structured Principal Component Analysis and Model Learning for Classification and Quality Detection of Rice Grains
In scientific and commercial fields associated with modern agriculture, the categorization of different rice types and determination of its quality is very important. Various image processing algorithms are applied in recent years to detect different agricultural products. The problem of rice classification and quality detection in this paper is presented based on model learning concepts includ...
متن کاملFinancial Reporting Fraud Detection: An Analysis of Data Mining Algorithms
In the last decade, high profile financial frauds committed by large companies in both developed and developing countries were discovered and reported. This study compares the performance of five popular statistical and machine learning models in detecting financial statement fraud. The research objects are companies which experienced both fraudulent and non-fraudulent financial statements betw...
متن کاملUsing Non-Archimedean DEA Models for Classification of DMUs: A New Algorithm
A new algorithm for classification of DMUs to efficient and inefficient units in data envelopment analysis is presented. This algorithm uses the non-Archimedean Charnes-Cooper-Rhodes[1] (CCR) model. Also, it applies an assurance value for the non-Archimedean using only simple computations on inputs and outputs of DMUs (see [18]). The convergence and efficiency of the ne...
متن کامل