Discovering Admissible Models of Complex Systems Based on Scale-Types and Idemtity Constraints
Discovering Admissible Models of Complex Systems Based on Scale-Types and Idemtity Constraints
复制标题
基于尺度类型和身份约束发现复杂系统的可接受模型
DOI：
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发表时间：
1997
期刊：
影响因子：
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通讯作者：
H. Motoda
中科院分区：
文献类型：
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作者：
T. Washio;H. Motoda
SDS是一个从数字测量数据中发现的系统。它在搜索效率、噪声容忍度、所得方程的可信度和它所能处理的目标系统的复杂性的各个方面都优于现有的系统。SDS的力量来自于使用测量数据的标度类型和恒等式的数学性质来约束可容许解。通过一个复杂的工作实例描述了它的算法，并讨论了与其他系统的性能比较。
SDS is a discovery system from numeric measurement data. It outperforms the existing systems in every aspect of search efficiency, noise tolerancy, credibility of the resulting equations and complexity of the target system that it can handle. The power of SDS comes from the use of the scale-types of the measurement data and mathematical property of identity by which to constrain the admissible solutions. Its algorithm is described with a complex working example and the performance comparison with other systems are discussed.