感知机

用感知机学习算法改善MUSIC测向算法的分辨率

Improvement of the Resolution of MUSIC Method with the Perceptron Training Algorithm

感知机的DNA计算模型

DNA-Based Model of Perceptron

基于多层感知机和RBF转换函数的混合神经网络

Hybrid Neural Network Based on Transfer Functions of Multilayer Perception and Radial Basis Function

一种基于L2范数的软核感知机感知机的DNA计算模型

Soft kernel perceptron in terms of L_2 norm DNA-Based Model of Perceptron

本神经网络算法用于动态心电信号的数据压缩。神经网络采用了反向传播算法（PB算法）的三层感知机。

A neural network algorithm for data compression of ambulatory ECG signals is proposed The neural network is a 3-layer perceptron with back propagation algorithm .

系统采用感知机（Perceptron）算法进行参数训练。

A perceptron algorithm is used in training parameters .

介绍了金刚石钻头设计专家系统中的配方设计子系统（BPFES），包括多层感知机数学模型和BP算法，专家子系统的设计，钻头配方参数样本的网络训练；

The bit prescription subsystem including multiple perceptron mathematics model and BP algorithm , expert designing subsystem and exercise of network of bit prescription parameter samples is emphasized on .

本文建立了在人工神经网络中实现简单的线性分类功能的、感知机的DNA计算的自装配模型，该模型并行地实现了神经网络的学习过程，充分利用了DNA计算极度并行的特点。

In this paper , we develop a DNA_based model to realize linear classifying function of perceptron in neural network . This model completes parallel learning courses of neural network and uses the utmost parallel feature of DNA computing adequately .

然后，在此基础上，应用神经网络理论中的感知机模型，对于不同的位字符空间，生成了相应的线性识别机器，PC仿真给出了良好的结果。

On the basis of that , create different linear recognition machines for characters in different spaces imitating the perceptive machine model in NN theory . The linear machines we designed have got better effect in practical application .

针对这一问题，开展了基于神经网络合并技术（NN）的研究.采用多层感知机神经网络模型，通过仿真实验确定了模型结构。

So , the NN ( neural network ) based combination technique is studied , which introduces the NN model with multilayer perceptron to determine model structure through simulation .

研究了采用多层感知机MLP网络实现语音起点检测的方法。

A new speech detection method using the MLP neural network is studied , including the structure of MLP NN and BP learning algorithm .

为了更有效地优化前向神经网络的求解能力，提出了一种新的综合的转换函数，将多层感知机和RBF神经网络更有机地结合起来，以产生灵活的决策边界。

In order to effectively optimizing the solution of feed-forward neural network , a new general transfer function is proposed that effectively unifies the inputs of multilayer perception and radial basis function to provide flexible decision border .

本文对于二元输入一阶感知机平均记忆容量C（n）（n为输入模式向量的维数）进行了估计，得出其上、下界分别是2n，1／2n。

In this paper is studied the average memory capacity of first-order perceptrons and it is estimated in the n - dim binary input case that an upper bound of it is 2n and a lower bound of it , 1 / 2 n.

本文将此理论扩展到非线性sigmoid神经元，分析了用来解决模式分类问题的、由sigmoid神经元构成的单隐层MLP（多层感知机）的内部行为；

Extending this theory , this paper analyzes the sigmoidal neurons and the internal behaviors of single hidden-layer perceptrons ( MLPs ) with sigmoid neurons trained for pattern classifications .

采用单层感知机网络、BP网络、径向基网络对汽车目标的特征数据进行识别，最后分别运用多数投票、平均Bayes、专家委员会三种融合算法把对各网络识别结果进行融合，得出最终判别结果。

Single layer perceptron neural network , BP neural network and RBF neural network are used to recognize vehicle target by features vector . Finally , the recognition result is fused by using the voting method , average Bayes classifier and committee of expert to get the final result .

基于这一事实，提出一种基于L2范数的软核感知机（SoftKernelPerceptron，SKP），将感知机算法直接用于求解L2范数软边缘算法决定的线性可分问题。

Based on this fact , we propose a Soft Kernel Perceptron ( SKP ) in terms of L2 norm , in which the regular perceptron is directly employed to solve the linearly separable problem determined by L2 norm soft margin algorithms .

实际数据库的测试结果表明，SKP算法能够有效地解决非线性问题，并且继承了感知机运算简单速度快的优点。

The experiments on some real datasets demonstrate that the proposed SKP can solve nonlinear classification efficiently . Moreover , it has the original advantage of simple calculation and fast speed .

关于单体模糊神经网络感知机收敛定理的讨论

On the Perceptron Convergence Theorem of the Monolithic Fuzzy Neural Networks

基于模糊均值聚类和感知机的网络银行客户挖掘

Internet Bank Customer Mining Based on Fuzzy c-means Clustering and Perceptron

层数对线性神经元感知机性能的影响

Influence of layers to performance of perceptron with linear activation function

基于多层感知机神经网络的智能高度传感器设计

Intelligent Altitude Sensor Based on Artificial Neural Networks Using Multilayer Perceptron

包含奇异类样本的感知机学习规则的单位圆算法

Unit Circle Algorithm of Perceptrons Study Rule with Oddity Sample

感知机神经网络模型构成一种通用非线性控制器

A Nonlinear Common Controller Based on Perception Model of Neural Network Computer

感知机只能解决线性可分问题。

The perceptron can only solve linearly separable problems .

简化的广义多层感知机模型及其学习算法

Simplified Generalized Multi-layer Perceptron Model and Its Learning Algorithm

一阶感知机平均记忆容量的界

The bounds of average memory capacity of first-order perceptrons

一种基于多层感知机的无监督异常检测方法

Unsupervised anomaly detection based on a multi-layer perceptron

利用多层感知机映射提高不匹配环境下的语音识别性能

Use the Multi-Layer Perceptron Mapping to Improve the Speech Recognition Performance under Unmatched Environments

感知机特别适用于简单的模式分类问题。比如线性可分问题。

Therefor , The perceptron is especially suitable for the simple pattern classification problem .

本文简述了图像代数以及它与数学形态神经网络的关系，并以此为基础建立了一个数学形态视觉感知机模型。

Image algebra and its relationship to morphological neural networks are discussed briefly in this paper .