Discretization

as a class. This method of partitioning continuous variables into classes is sometimes termed discretization. Sadly, the quantity of how to discretize a continual attribute is infinite. Discretization could be a potential long bottleneck, since the variety of attainable discretization is exponential within the number of interval threshold candidates at intervals the domain [14]. The goal of discretization is to seek out a collection of cut points to partition the range into a little variety of intervals

As discussed in the introduction, we use RBF-LA (RBF learning automata) to learn human strategies. Accordingly, we will first introduce the classify strategies, then explain court discretization which is necessary for the tennis model. Finally, we will show how the episodes and states features in the tennis task can be represented. A. Classify Strategies Tennis is a racquet sport that can be played either against a single opponent (singles) or between two teams of two players (doubles). For simplicity

Action-Based Discretization for AI Search Dr. Todd W. Neller* Department of Computer Science Gettysburg College Campus Box 402 Gettysburg, PA 17325-1486 Introduction As computer gaming reaches ever-greater heights in realism, we can expect the complexity of simulated dynamics to reach further as well. To populate such gaming environments with agents that behave intelligently, there must be some means of reasoning about the consequences of agent actions. Such ability to seek out the ramifications

The second order upwind discretization model was used in this problem. The truncated error due to selected terms in the Taylor series expansion is reduced and a more accurate solution is implied. Fewer grid points are necessary to give the same level of accuracy. ∂u/∂x+u=0,〖(du/dx)〗_i + ui =0 u_(i-1)=u_i-∆x(du/dx)_i+(∆x^2)/2 ((d^2 u)/(dx^2 ))_i-(∆x^3)/6 ((d^3 u)/(dx^3 ))_i+0(∆x^4) u_(i-1)=u_i+∆x(du/dx)_i+(∆x^2)/2 ((d^2 u)/(dx^2 ))_i-(∆x^3)/6 ((d^3 u)/(dx^3 ))_i+0(∆x^4) The central differencing method

on the time discretization parameter. In the second step, those problems are discretized in space. The main advantage of this technique is that it permits to analyze independently the contribution to the error of the time and space discretizations. Nevertheless, the method of \cite{221} only gives first order of uniform convergence in time variable. However, it is possible to combine this idea with the use of the Richardson extrapolation technique applied only to the time discretization. Then, the

Abstract—In this article a new and accurate digital approximation of the fractional-order differentiator (FOD) in the form of FIR filter is presented. This approach is based on power series expansion of fractional order systems. First, the first-order digital differentiators can be simply derived from a classical continuous-time approximate differentiator by using the Bilinear transformation. Then, the transfer function of digital FOD is obtained by taking fractional power of the transfer function

A flat plate solar collector has a dynamic behaviour in response to variations in the intensity of solar radiation at different times of the day and also variations in weather conditions. The characteristics governing the input-output behaviour of a flat plate collector can be described by a mathematical model which serves as a prerequisite for simulation and control. The steady state and transient characteristics of flat plate solar collectors have been studied in \cite{Hilmer1999Solar,Dhariwal2005Solar

Constant frequency assumption The phasor method is correct for steady-state solutions, but during transients, using the constant frequency !o is an approximation to the actual grid frequency, !. However, if the power network remains close to fo (±0:2-0:5Hz[2]), using both the constant frequency phasor solution for the electrical network(Eq.(1.8)) and the constant frequency in the electromechanical equation (Eq.(1.5)) helped to alleviate the tedious mathematical calculations in the early power system

The laminate is directly modelled with discrete fiber orientation angles at each point in the structure yielding different stacking sequences. The laminate is usually discretized based on the underlying discretization of the structure such as the finite element [17] or cellular automata discretization [18]. Several authors have used direct fiber orientation angle modelling to design variable stiffness laminates. Hyer and Charrete were among the first to investigate variable stiffness laminates by aligning

Research paper Infectious and deadly diseases have been known to spread over social networks of people and animals. Network epidemiology has been proved as an indispensable approach for understanding epidemics of infectious diseases and is often used in medical epidemiology and network science. The most famous and practical example of network epidemiology is the GLEAMviz platform, which succeeded in forecasting the 2009 H1N1 pandemic, saving millions of lives. Behind network epidemiology, one can