The cross-entropy and minimum cross-entropy methods are well-known Monte Carlo simulation techniques for rare-event probability estimation and optimization. In this paper we investigate how these methods can be extended to provide a general non-parametric cross-entropy framework based on φ-divergence distance measures. We show how the χ distance in particular yields a viable alternative to Kull...

In this study we illustrate a Maximum Entropy (ME) methodology for modeling incomplete information and learning from repeated samples. The basis for this method has its roots in information theory and builds on the classical maximum entropy work of Janes (1957). We illustrate the use of this approach, describe how to impose restrictions on the estimator, and how to examine the sensitivity of ME...

Elimination of the ambiguity is the key to the computational translation. In order to find an effective way to improve the accuracy of the computational translation, the cross entropy was introduced. After the analysis of the reasons of the low accuracy, the information entropy was introduced into the disambiguation. The practice of ambiguity elimination shows the method has high accuracy and t...

This paper investigates the multiple unmanned aerial vehicle (multi-UAV) cooperative task assignment problem. Specifically, we assign different types of UAVs to accomplish classification, attack, and verification tasks targets under resource, precedence, timing constraints. Due complex coupling among these tasks, decompose considered problem into two subproblems: one with continuous independent...

Abstract Optimal power flow (OPF) is a complex, highly nonlinear, NP-hard optimization problem, in which the goal to determine optimal operational parameters of power-related system (in many cases type smart or micro grid) guarantee an economic and effective dispatch. In recent years, number approaches based on metaheuristics algorithms have been proposed solve OPF problems. this paper, we prop...

Background: The purpose of this study was to determine if trained runners exhibited different complexity of walking than untrained individuals. Methodology: Trained runners (T) and untrained controls (UT) performed two incremental walking trials that spanned 2, 4 and 6 km/h. Complexity was assessed through control entropy (CE), which was determined from high resolution accelerometry collected f...

We discuss a one-parameter family of generalized cross entropy between two distributions with the power index, called the projective power entropy. The cross entropy is essentially reduced to the Tsallis entropy if two distributions are taken to be equal. Statistical and probabilistic properties associated with the projective power entropy are extensively investigated including a characterizati...

The objective of this paper is to propose a spatial disaggregation procedure that will be useful in assessing economic and environmental impacts of the last CAP reform of EU agriculture(e.g., the study of water pollution due to agriculture). The proposed disaggregation method uses agricultural data in conjunction with biophysical processes to break down agricultural FADN data available at the r...

The fuzzy cross entropy of intuitionistic fuzzy sets, so-called intuitionistic cross entropy, is introduced by analogy with the cross entropy of probability distributions. This paper introduces the concepts of discrimination information and cross-entropy in the intuitionistic fuzzy sets. Based on this improved entropy measure, we add information of hesitation and reveal an intuitive and mathema...

We introduce a new randomized method called Model Reference Adaptive Search (MRAS) for solving global optimization problems. The method works with a parameterized probabilistic model on the solution space and generates at each iteration a group of candidate solutions. These candidate solutions are then used to update the parameters associated with the probabilistic model in such a way that the ...