APRIS: Library and On-Line Tools to Drive Parallel Adaptive and Neural Heuristic to Improve System Identification Precision

J. Pulido, M. Rodríguez, J.M.S. Ferez, M. Simon, J. Criado
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Abstract

In this paper are shown the methodologies and tools developed to increase the accuracy of the system identification as method of modelization, simulation and prediction of the behaviour of dynamic systems, so much of the type single-input-single-output as the well known time series. These developments are mainly based on an adaptive parallel algorithm. The adaptive part consists of the evolution of its main parameter through the time, so it self-adjusts to offer good solutions. The parallel part consists of the implementation of processing units of parallel running. The codes have been programmed in Matlab language, organizing themselves in a free and public domain library, which is handled by a set of graphic tools through Web services for its on-line control and experimentation
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库和在线工具驱动并行自适应和神经启发式,以提高系统识别精度
本文展示了为提高系统识别的准确性而开发的方法和工具,作为动态系统行为的建模、仿真和预测方法,其中很多是单输入-单输出类型,如众所周知的时间序列。这些发展主要基于自适应并行算法。自适应部分包括其主要参数随时间的演变,因此它可以自我调整以提供良好的解决方案。并行部分由实现并行运行的处理单元组成。代码用Matlab语言编写,组织成一个免费的公共领域库,由一组图形工具通过Web服务处理,实现在线控制和实验
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