A method of constructing knowledge based fuzzy perceptron based on rough sets theory is proposed.
提出了用粗糙集理论构造模糊多层感知器的方法。
The extracted initial rules and their accuracy and coverage are used to configure the fuzzy perceptron structure and initial weights for training.
网络的结构由已经抽取的规则映射而成，初始连接权由规则的精确度和覆盖度确定。
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 paper introduces a fuzzy classification model based on the proposed fuzzy kernel hyperball perceptron(FKHP) learning method.
本文提出一种模糊核超球感知器（FKHP）学习方法，并介绍了一种基于FKHP这种学习方法的模糊分类模型。
This paper introduces a fuzzy classification model based on the proposed fuzzy kernel hyperball perceptron(FKHP) learning method.
本文提出一种模糊核超球感知器（FKHP）学习方法，并介绍了一种基于FKHP这种学习方法的模糊分类模型。
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