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感知机只能解决线性可分问题。The perceptron can only solve linearly separable problems.

这里有一些与感知器算法相区别的重要不同点。There are important differences from the perceptron algorithm.

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第二种基于自适应隶属度函数的多层感知器，是对前一种方法的近似。The second one is a multilayer perceptron based on auto adaptive fuzzy membership functions.

仿真结果表明该方法设计的3型FIR线性相位多带通滤波器的性能接近于理想滤波器。A multilayer perceptron network for the design of the FIR multi-band-pass filters of type-3 are proposed.

最后，介绍了感知器神经网络的特点，提出运用单层感知器网络进行帘子布疵点检测。Finally, the theory of perceptron neural network is introduced and applied to cord defects identification.

经过我选择了多层感知和标准的反向传播训练算法的研究。After some research I have chosen a multilayer perceptron and standard back-propagation algorithm for training.

考虑到线性模型的一些缺点，本文随后应用神经网络理论，分别建立感知器预警模型和BP网络预警模型。Because liner models have some defects, I construct perceptron model and BP model on base of neural networks theory.

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后非线性盲分离系统中的非线性部分用一个多层感知器模拟。The nonlinear sub-system of the post-linear blind source separation structure is modeled by a multilayer perceptron.

我们在此建议使用利用倒传递网路训练的多层次认知元来克服这个困难。To overcome those difficulties, we suggest the multiplayer perceptron trained by a back propagation as our recognizer.

数值实验表明NNKBN模型在许多方面优于传统的多层感知器模型。Numerical experiments show that the NNKBN model has many advantages over the conventional multi-layer perceptron model.

线性口袋算法改进了线性感知器算法，能够直接处理线性不可分问题。The linear pocket algorithm improves the perceptron algorithm and can deal with the linearly non-separable problems directly.

感知器是一种有用的神经网络模型，可以对线性可分的模式进行正确分类。Perceptron is a kind of useful neural network model and can classify the classification of the detachable linearity correctly.

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利用感知器异或函数获得了节点之间不断优化的连接关系，然后得到最优路径图。The continually optimized connecting relation is gained via perceptron and XOR function, then the optimal path graph is found.

原因探究、应机学习规则、敛定理。梯度跟随学习法、上学习。The Problem of Credit Assignment. Perceptron Learning Rule. Convergence Theorem. ? Learning by Gradient Following. Online learning.

介绍一种用循环多层感知器神经网络实现符号逻辑推理系统的方法。A method of implementing symbol logic inference system using recurrent multilayer perceptron neural networks is presented in this paper.

本文研究了非高斯噪声中信号的检测，采用多层感知器神经网络作为检测器。In this paper, the authors study the detection of signals in non-Gaussian noise, and employ a multilayer perceptron neural network as a detector.

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。The extracted initial rules and their accuracy and coverage are used to configure the fuzzy perceptron structure and initial weights for training.

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本文将感知器原理与自适应算法的基本思想结合在一起，构造了一种新的算法，解决了该问题。This article constructs an algorithm which integrates the theory of perceptron with the basic idea of self-adaptive algorithm to resolve this problem.

本文在特征抽取的基础上，采用BP网络进行分类，并附加线性感知器来实现单字的有效识别。This paper applies BP Neural Network to classification based on feature extraction and with a linear perceptron for recognition of individual characters.

基于系统识别理论，采用ARX模型和优化神经网络技术对磁流变阻尼器的性能进行了仿真。In this paper, the performance of MR damper is simulated based on system identification with optimal multi-layer perceptron neural networks and ARX model.