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首页> 外文期刊>Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on >A General CPL-AdS Methodology for Fixing Dynamic Parameters in Dual Environments
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A General CPL-AdS Methodology for Fixing Dynamic Parameters in Dual Environments

机译：双重环境中固定动态参数的通用CPL-AdS方法

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摘要

The algorithm of Continuous Point Location with Adaptive $d$ -ary Search (CPL-AdS) strategy exhibits its efficiency in solving stochastic point location (SPL) problems. However, there is one bottleneck for this CPL-AdS strategy which is that, when the dimension of the feature, or the number of divided subintervals for each iteration, $d$ is large, the decision table for elimination process is almost unavailable. On the other hand, the larger dimension of the features $d$ can generally make this CPL-AdS strategy avoid oscillation and converge faster. This paper presents a generalized universal decision formula to solve this bottleneck problem. As a matter of fact, this decision formula has a wider usage beyond handling out this SPL problems, such as dealing with deterministic point location problems and searching data in Single Instruction Stream–Multiple Data Stream based on Concurrent Read and Exclusive Write parallel computer model. Meanwhile, we generalized the CPL-AdS strategy with an extending formula, which is capable of tracking an unknown dynamic parameter $lambda^{ast}$ in both informative and deceptive environments. Furthermore, we employed different learning automata in the generalized CPL-AdS method to find out if faster learning algorithm will lead to better realization of the generalized CPL-AdS method. All of these aforementioned contributions are vitally important whether in theory or in practical applications. Finally, extensive experiments show that our proposed approaches are efficient and feasible.
机译：具有自适应$ d $ ary搜索（CPL-AdS）策略的连续点定位算法显示了其解决随机点定位（SPL）问题的效率。但是，此CPL-AdS策略存在一个瓶颈，那就是，当特征的维数或每次迭代的划分子间隔数$ d $很大时，消除过程的决策表几乎不可用。另一方面，特征$ d $的较大尺寸通常可以使CPL-AdS策略避免振荡并收敛得更快。本文提出了解决该瓶颈问题的通用通用决策公式。实际上，该决策公式的使用范围比处理此SPL问题要广泛，例如处理确定性点定位问题以及在基于并行读取和互斥并行计算机模型的单指令流-多数据流中搜索数据。同时，我们使用可扩展的公式推广了CPL-AdS策略，该公式能够在信息性和欺骗性环境中跟踪未知的动态参数$ lambda ^ {ast} $。此外，我们在广义CPL-AdS方法中采用了不同的学习自动机，以找出更快的学习算法是否可以更好地实现广义CPL-AdS方法。无论在理论上还是在实际应用中，上述所有这些贡献都是至关重要的。最后，大量实验表明我们提出的方法是有效且可行的。

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