Multi-level weighted fuzzy classifier is proposed.
④多级加权模糊分类器的提出。
The classification technique uses various features of the signal amplitude, frequency, and power spectrum applied to the fuzzy classifier.
本文研究了一种基于模糊分类的调制信号识别方法，即提取信号时域、频域、功率谱等统计特性，利用模糊分类器进行分类识别。
For a neuro_fuzzy classifier based on the fuzzy perceptron, this paper analyses how membership function constraints affect the classification result.
针对一类基于模糊感知器的神经模糊分类器，分析了隶属函数限制条件对分类结果的影响。
This genetic-fuzzy classifier based on fuzzy associative rules can make accurate judgments without enough evidence to improve the performance of the IDS.
该模型在证据不充分的情况下能够更快速、正确地判断入侵事件，从而进一步提高检测的效率。
The experimental results show that the proposed fuzzy classifier based on AFS theory and Genetic Algorithm has few rules, high classification rate, and good interpretability.
从实验结果可以看出将两者结合设计出的模糊分类器具有分类准确率高、模糊描述简单、规则少且易于理解等特点。
This fuzzy system design method that uses a fuzzy rule to represent a cluster is then propsed so that a fuzzy classifier can be efficiently constructed to correctly classify the training data.
用这样一个模糊规则来表示分类的模糊系统，更加有效地构建了一个能对训练样本比较准确分类的模糊分类器。
This paper presents a multiclass neural network classifier to learn disjunctive fuzzy information in the feature space.
本篇论文提出一个类神经网路分类器来学习多类的分离模糊资讯。
Based on Bayesian optimal classifier, combining with rough set and fuzzy set, a new transformer fault diagnosis and maintenance mode is presented in the paper.
基于粗糙集、模糊集和贝叶斯最优分类器，提出一种变压器绝缘故障诊断与维护的综合决策模型。
A neural network classifier that utilizes fuzzy sets as failure classes of a liquid propellant rocket engine is studied.
研究了一种用模糊集表示火箭发动机故障模式的神经网络分类器。
This paper, based on the theory and method of artificial nerve networks and fuzzy sets, puts forward a classifier model used for diagnosing the quasi-heath state.
利用人工神经网络与模糊集的理论和方法提出了诊断亚健康状态的一种分类器模型。
As an important classifier, fuzzy clustering technique has been widely used in segmentation of MRI image and became an effective segmentation tool of MR image.
作为一种重要的分类器，模糊聚类技术在磁共振图像的分割中已经得到了成功的应用，并成为了一种有效的磁共振图像的分割工具。
For this object, a method of determining fuzzy integral density with membership matrix is proposed, and the classifier ensemble algorithm based on fuzzy integral is introduced.
给出了基于隶属度矩阵的模糊积分密度确定方法，介绍了基于模糊积分的分类器集成算法。
Then patterns are categorized by the designed fuzzy inference regulation. The design of this piecewise linear classifier enhances the ability of linear classification of the algorithm.
然后，通过设计的模糊推理规则进行模式的分类，这种分段线性分类器的设计提高了算法线性分类的能力。
A model of rough fuzzy neural network classifier was presented by combining rough set and fuzzy neural network.
结合粗糙集和模糊神经网络提出了一种粗糙模糊神经网络识别器的模型。
The main contribution of this dissertation includes four aspects. They are instantaneous parameters extraction, fuzzy feature selection, single classifier design and combined classifier design.
本文主要工作体现在瞬时参数的提取、模糊特征选择、单个分类器设计和组合分类器设计这四个方面。
As a nonlinear method, the fuzzy rule-based pattern recognition has good comprehensibility, but has not been applied to the multiple classifier fusion.
而基于模糊规则的模式识别方法是一类可理解性好的非线性方法，但迄今为止还没有被应用于多分类器融合问题中。
In this paper a model of discrete fuzzy membership function based on statistical distribution of features of pattern is presented. It is used for the fuzziness of input features of classifier.
本文提出了一种基于模式类特征空间统计分布的模糊隶属度函数模型，可有效地反映模式在特征空间中的真实分布，用于模式分类器输入特征的模糊化可获取更好的识别性能。
SFAM is an incremental neural network classifier. It is a simple and fast version of Fuzzy ARTMAP (FAM). Both FAM and SFAM produce the same output given the same input.
SFAM是一个改进版神经网络分离器，是模糊ARTMAP的简化和快速版本。对于相同的输入FAM和SFAM具有相同的输出。
SFAM is an incremental neural network classifier. It is a simple and fast version of Fuzzy ARTMAP (FAM). Both FAM and SFAM produce the same output given the same input.
SFAM是一个改进版神经网络分离器，是模糊ARTMAP的简化和快速版本。对于相同的输入FAM和SFAM具有相同的输出。
应用推荐