Programming Techniques for Considering m Desired Conditions from n Possible Conditions

Surapon Riyana, N. Homdoung, Kittikorn Sasujit
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Abstract

The performance of computer programs (or the hardware that can be programmed such as IoTs, embedded computers, and PLCs) is generally based on the complexity of the particular program development technique. High complexity often uses more execution times and system resources. For this reason, the computer program is less computational complexity to be desired. The conditional statements tell computers what certain information is a major cause of computer program complexities, e.g., considering m desired conditions from n possible conditions. To achieve this aim in computer programs, the data combination is often utilized. However, it is high complexity. Moreover, they cannot give that one condition takes precedence over others. To rid these vulnerabilities of combined conditions, a simple programming technique for considering m desired conditions from n possible conditions is proposed in this work, which is based on the summation of the condition weights. It only has the complexity of search spaces and data constructions to be O(n) and each condition can be set to be different precedence from the others. Furthermore, the proposed technique is evaluated by extensive experiments. From the experimental results, they indicate that the proposed technique is more effective and efficient than the comparative technique.
从n个可能条件中考虑m个期望条件的编程技术
计算机程序(或可编程的硬件,如物联网、嵌入式计算机和plc)的性能通常基于特定程序开发技术的复杂性。高复杂性通常使用更多的执行时间和系统资源。由于这个原因,计算机程序的计算复杂度较低。条件语句告诉计算机哪些信息是计算机程序复杂性的主要原因,例如,从n个可能条件中考虑m个期望条件。为了在计算机程序中达到这一目的,通常使用数据组合。然而,它是高复杂性的。此外,他们不能给出一个条件优先于其他条件。为了消除组合条件的这些弱点,本文提出了一种基于条件权重之和的简单规划技术,从n个可能条件中考虑m个期望条件。它只有搜索空间和数据结构的复杂度为O(n),并且每个条件可以设置为不同的优先级。此外,通过大量的实验对所提出的技术进行了评估。实验结果表明,该方法比比较方法更有效,效率更高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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