A novel hybrid neural fuzzy inference system is presented. Only based on the desired input output data pairs, are the knowledge acquisition and initial fuzzy rule sets available.
设计一种新型混合模糊神经推理系统，该系统仅从期望输入输出数据集即可达到获取知识、确定模糊初始规则基的目的。
Based on the input-output data of the system, a new method to create fuzzy rules by dividing the input space was proposed and ap plied to modelling of the non-linear drying process.
基于系统的输入-输出数据，提出一种通过划分输入空间提取模糊规则的方法，并将该方法应用于建模非线性程度较高的干燥过程。
Experimental modeling, or modeling by synthesis, is the selection of mathematical relationship which seems to fit observed input-output data.
经验建模，或叫综合建模，是一种通过寻找数学关系而建立模型的方法，这种数学关系应该能够与输入-输出观测数据相配合。
Nonlinear physical parameters of the docking mechanism-inertial, damping and stiffness matrices, are identified by direct parameter estimate(DPE)method with input-output data of the system.
采用直接参数识别方法（DPE）由系统的输入、输出数据识别空间对接机构的物理参数：惯量矩阵、阻尼矩阵和刚度系数矩阵。
However, the SSNN models required offline training with large data sets of input-output data.
然而，状态空间神经网络需要大量的历史数据作为离线训练之用。
The good localization characteristics of wavelet functions in both time and frequency space allowed hierarchical multi-resolution learning of input-output data mapping.
利用小波变换所具有的良好的时频分析特性，实现了输入输出之间映射关系的多分辨学习。
The good localization characteristics of wavelet functions in both time and frequency space allow hierarchical multi-resolution learning of input-output data mapping.
由于小波变换在时间和频率空间具有良好的定位特性，使小波神经网络可对输入、输出数据进行多分辨的学习训练。
The good localization characteristics of wavelet functions in both time and frequency space allow hierarchical multi-resolution learning of input-output data mapping.
由于小波变换在时间和频率空间具有良好的定位特性，使小波神经网络可对输入、输出数据进行多分辨的学习训练。
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