基于受限玻尔兹曼机的条件概率计算及其在系统辨识中的应用
Conditional probability calculation using restricted Boltzmann machine with application to system identification
AI总结：
本文利用受限玻尔兹曼机计算条件概率用于非线性系统辨识，通过二进制编码和连续值方法改进模型，提出通用逼近分析，验证在噪声大和系统动态复杂时方法优势。
摘要：
使用概率方法进行非线性系统辨识具有优势，如数据集中的噪声和离群值对概率模型影响小，输入特征可概率形式提取。概率模型的主要障碍是概率分布难以获得。本文将非线性系统辨识转化为求解条件概率问题，修改受限玻尔兹曼机（RBM），使联合概率、输入分布和条件概率可通过RBM训练计算。讨论了二进制编码和连续值方法，提出基于条件概率建模的通用逼近分析。使用两个非线性系统基准测试比较本文概率建模方法与其他黑盒建模方法。结果表明，该新方法在存在大量噪声和系统动态复杂时表现更优。
英文摘要：
There are many advantages to use probability method for nonlinear system identification, such as the noises and outliers in the data set do not affect the probability models significantly; the input features can be extracted in probability forms. The biggest obstacle of the probability model is the probability distributions are not easy to be obtained. In this paper, we form the nonlinear system identification into solving the conditional probability. Then we modify the restricted Boltzmann machine (RBM), such that the joint probability, input distribution, and the conditional probability can be calculated by the RBM training. Binary encoding and continue valued methods are discussed. The universal approximation analysis for the conditional probability based modelling is proposed. We use two benchmark nonlinear systems to compare our probability modelling method with the other black-box modeling methods. The results show that this novel method is much better when there are big noises and the system dynamics are complex.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
