一种新型的感应电机速度辨识策略

介绍了一种基于神经网络的感应电机速度估计策略。应用反向传播算法的神经网络实时辨识电机速度,其目标函数是目标模型和神经网络模型输出之间差的平方和。速度作为神经网络的一个权值,通过反向传播算法来调节使之精确地跟踪实际的电机速度,实验结果表明此方案是可行和有效的。     

基于动态递归反馈型神经网络的永磁同步电机 转矩观测器设计

现有永磁同步电机普遍存在算法复杂、电机参数辨识困难、电磁转矩难以通过数学模型来精 确估算等问题,从而导致电机控制精度以及驱动系统的整体性能下降。该研究设计了一种基于动态递 归反馈型神经网络的电机电磁转矩网络拓扑模型,使用 MATLAB/Simulink 将该神经网络封装成转矩观 测器,并用于电机转矩的精确估算。实验结果显示,与传统转矩和反向传播神经网络计算方式相比, 该研究所设计的转矩观测器具有更高的转矩计算精度,与反向传播神经网络算法相比具有更高的控制 精度与准确性。     

一种新的单张照片三维人脸重建方法

针对利用反向传播神经网络实现单张照片三维人脸重建方法中速度慢、精度低的缺点,提出了一种改进反向传播神经网络,该方法通过引入混沌变异思想有效解决了APSO算法中的早熟问题,然后以此APSO算法取代传统反向传播神经网络的梯度下降算法,通过对基于传统反向传播神经网络和改进反向传播神经网络的单张照片三维人脸重建方法对比实验,结果表明,采用改进后的反向传播神经网络算法实现的单张照片三维人脸重建,重建的三维人脸更加逼真,并且模型重建的效率和精度也得到了明显提高,重建的时间也大大减少.     

基于蝙蝠算法优化反向传播神经网络模型的无线网络流量预测

针对无线网络流量数据预测精度不高问题,提出一种基于蝙蝠算法(BA)优化的反向传播(BP)神经网络的分类预测模型——BABP.通过采用蝙蝠算法对BP神经网络模型的初始权值与阈值进行全局寻优,构建崭新的基于蝙蝠算法优化的神经网络模型.通过与基于传统寻优算法遗传算法(GA)与粒子群优化(PSO)算法的反向传播(BP)神经网络模型比较,在无线网络流量数据的分类预测和稳定性方面,提出的BABP模型要优于GABP模型、PSOBP模型;同时,无论迭代次数的多与少,BABP均比GABP、PSOBP算法更快地收敛.实验结果表明,BABP模型在预测精度、寻优速度以及模型稳定性等方面均比GABP、PSOBP模型更具优势.     

基于模糊神经网络驼峰溜放速度的控制

提出了一种基于模糊神经网络理论求得动态出口速度的模型，使用改进的误差反向传播学习算法进行学习，具有很好的自学习和自适应能力，能根据列车的溜放实际情况自动调整减速器出口速度，仿真结果表明，模糊神经网络用在驼峰溜放速度控制中有很好的效果。     

基于神经网络算法的企业财务预警模型构建

本文详细介绍了基于神经网络算法的企业财务预警模型构建思路,明确提出了财务预警机制中的各项指标,在财务预警基本模型的基础上通过人工神经网络实施了进一步的优化,解决了常规反向误差传播算法收敛速度过慢的问题.     

文本分类的神经网络模型

给出一种与文档段落结构相关联的文本分类神经网络模型。描述神经网络的训练算法,包括正向传播算法和反向修正算法。对于算法的主要步骤,给出了更详细计算方法。最后给出了神经网络模型性能测试结果。     

改进神经网络的传感器故障诊断与容错处理

为提高神经网络对霍尔传感器发生故障诊断率,设计了一种改进神经网络模型,该模型由反向传播(BP)神经网络并行组成,利用算法对各BP神经网络输出进行加权整合,进而得到误差更小的输出结果,并将改进神经网络模型应用于无刷直流电机霍尔传感器故障诊断系统中,利用无位置传感器系统实现霍尔传感器故障容错处理。仿真结果表明:改进神经网络故障诊断准确率达到96%,高于传统BP神经网络,且容错控制系统能够显著降低霍尔传感器故障对电机转速的影响,使电机能够在霍尔传感器故障时正常稳定运行。     

结合聚类思想神经网络文本分类技术研究*

针对传统的基于神经网络文本分类算法收敛速度慢等缺点,在分析了文本分类系统的一般模型,以及在应用了互信息量的特征提取方法提取特征项后,提出了一种基于样本中心的径向基神经网络文本分类算法;并引入了聚类算法的核心思想,改进误差反向传播神经网络分类算法收敛速度较慢的缺点。实验结果表明,提出的改进算法与传统的BP神经网络分类算法相比,具有较高的运算速度和较强的非线性映射能力,在收敛速度和准确程度上也有更好的分类效果。     

基于Mel倒谱和BP神经网络的船舶目标分类研究

Mel倒谱系数分析依据人耳听觉特性,可以提取有利于船舶目标分类的特征.前向神经网络的反向传播算法对类别数目小但分类困难的模式识别问题有良好的分类效果.针对Mel倒谱系数分析提取的船舶目标分类识别特征,采用前向神经网络的反向传播算法,可以有效对船舶目标进行分类.     

感应电机矢量控制系统的神经网络转速辨识

为解决感应电机无速度传感器矢量控制系统的转速辨识问题,在给定的无速度传感器感应电机间接矢量控制系统中,根据感应电机的数学模型,经过一定的变换,利用电机易于检测到的定子电压和电流,以及基于BP算法的两层神经网络,用期望状态与实际状态之间的偏差来调整神经网络模型的权值,达到实时辨识电机转速的目的。该方法简单、直观,不仅利用了神经网络的优点,又能适应感应电机调速系统实时控制的要求。仿真结果验证了该方法的有效性。     

未知光源位置环境中物体形状恢复的神经网络方法研究

用神经网络方法解决未知光源位置环境中物体三维形状恢复的问题.对漫反射表 面,用神经网络方法由已知表面形状物体及其对应图像的灰度值进行学习,所得权值可视为 环境光源参数.由此可恢复同样光源环境中其它物体的三维形状.实验证明,神经网络方法可 以解决未知光源位置环境(包括多个光源)中漫反射表面物体的三维形状恢复问题.     

PSO-optimized modular neural network trained by OWO-HWO algorithm for fault location in analog circuits

Fault diagnosis of analog circuits is a key problem in the theory of circuit networks and has been investigated by many researchers in recent decades. In this paper, an active filter circuit is used as the circuit under test (CUT) and is simulated in both fault-free and faulty conditions. A modular neural network model is proposed in this paper for soft fault diagnosis of the CUT. To optimize the structure of neural network modules in the proposed scheme, particle swarm optimization (PSO) algorithm is used to determine the number of hidden layer nodes of neural network modules. In addition, the output weight optimization–hidden weight optimization (OWO-HWO) training algorithm is employed, instead of conventional output weight optimization–backpropagation (OWO-BP) algorithm, to improve convergence speed in training of the neural network modules in proposed modular model. The performance of the proposed method is compared to that of monolithic multilayer perceptrons (MLPs) trained by OWO-BP and OWO-HWO algorithms, K-nearest neighbor (KNN) classifier and a related system with the same CUT. Experimental results show that the PSO-optimized modular neural network model which is trained by the OWO-HWO algorithm offers higher correct fault location rate in analog circuit fault diagnosis application as compared to the classic and monolithic investigated neural models.     

Control of a loaded induction machine using a feedforward neural network

Neural networks have been the subject of great interest in the control field. They seem to offer good ability to solve complex tasks. In this paper, some results of using a neural network for the nonlinear control of an induction machine's speed are presented. The neural controller is a feedforward neural network identified off-line. The backpropagation learning algorithm has been used for the off-line identification of the plant inverse neural model which provides the control action. Simulations under measurement noise and environmental condition variations have been investigated and comparison of performance has been made with an adaptive neural controller. The obtained results show the efficiency and the implementation simplicity of the presented strategy     

Predicting Aflatoxin Contamination in Peanuts: A Genetic Algorithm/Neural Network Approach

Aflatoxin contamination in peanut crops is a problem of significant health and financial importance. Predicting aflatoxin levels prior to crop harvest is useful for minimizing the impact of a contaminated crop and is the goal of our research. Backpropagation neural networks have been used to model problems of this type, however development of networks poses the complex problem of setting values for architectural features and backpropagation parameters. Genetic algorithms have been used in other studies to determine parameters for backpropagation neural networks. This paper describes the development of a genetic algorithm/backpropagation neural network hybrid (GA/BPN) in which a genetic algorithm is used to find architectures and backpropagation parameter values simultaneously for a backpropagation neural network that predicts aflatoxin contamination levels in peanuts based on environmental data. Learning rate, momentum, and number of hidden nodes are the parameters that are set by the genetic algorithm. A three-layer feed-forward network with logistic activation functions is used. Inputs to the network are soil temperature, drought duration, crop age, and accumulated heat units. The project showed that the GA/BPN approach automatically finds highly fit parameter sets for backpropagation neural networks for the aflatoxin problem.     

Towards the Optimal Learning Rate for Backpropagation

A backpropagation learning algorithm for feedforward neural networks withan adaptive learning rate is derived. The algorithm is based uponminimising the instantaneous output error and does not include anysimplifications encountered in the corresponding Least Mean Square (LMS)algorithms for linear adaptive filters. The backpropagation algorithmwith an adaptive learning rate, which is derived based upon the Taylorseries expansion of the instantaneous output error, is shown to exhibitbehaviour similar to that of the Normalised LMS (NLMS) algorithm. Indeed,the derived optimal adaptive learning rate of a neural network trainedby backpropagation degenerates to the learning rate of the NLMS for a linear activation function of a neuron. By continuity, the optimal adaptive learning rate for neural networks imposes additional stabilisationeffects to the traditional backpropagation learning algorithm.     

直流无刷电机智能控制系统研究

阐述直流无刷电机工作原理,分析直流无刷电机的数学模型;介绍模糊控制理论与神经网络控制理论,提出模糊自适应PID控制策略;在MATLAB环境下,分别使用反电动势建模法建立直流无刷电机控制系统的模型,并对各个模型进行仿真分析。然后利用BP神经网络控制策略,模糊自适应PID控制策略改进速度控制器中的常规PID算法,进行仿真,并将所得结果进行对比。从对比结果可以得出模糊自适应PID控制策略更适合直流无刷电机的控制。     

Training neural networks with additive noise in the desired signal

A global optimization strategy for training adaptive systems such as neural networks and adaptive filters (finite or infinite impulse response) is proposed. Instead of adding random noise to the weights as proposed in the past, additive random noise is injected directly into the desired signal. Experimental results show that this procedure also speeds up greatly the backpropagation algorithm. The method is very easy to implement in practice, preserving the backpropagation algorithm and requiring a single random generator with a monotonically decreasing step size per output channel. Hence, this is an ideal strategy to speed up supervised learning, and avoid local minima entrapment when the noise variance is appropriately scheduled.     

A neural network filter to detect small targets in high clutterbackgrounds

The detection of objects in high-resolution aerial imagery has proven to be a difficult task. In the authors' application, the amount of image clutter is extremely high. Under these conditions, detection based on low-level image cues tends to perform poorly. Neural network techniques have been proposed in object detection applications due to proven robust performance characteristics. A neural network filter was designed and trained to detect targets in thermal infrared images. The feature extraction stage was eliminated and raw gray levels were utilized as input to the network. Two fundamentally different approaches were used to design the training sets. In the first approach, actual image data were utilized for training. In the second case, a model-based approach was adopted to design the training set vectors. The training set consisted of object and background data. The neuron transfer function was modified to improve network convergence and speed and the backpropagation training algorithm was used to train the network. The neural network filter was tested extensively on real image data. Receiver operating characteristic (ROC) curves were determined in each case. The detection and false alarm rates were excellent for the neural network filters. Their overall performance was much superior to that of the size-matched contrast-box filter, especially in the images with higher amounts of visual clutter.     

A novel parametric fuzzy CMAC network and its applications

This paper shows fundamentals and applications of the novel parametric fuzzy cerebellar model articulation controller (P-FCMAC) network. It resembles a neural structure that derived from the Albus CMAC algorithm and Takagi–Sugeno–Kang parametric fuzzy inference systems. The Gaussian basis function is used to model the hypercube structure and the linear parametric equation of the network input variance is used to model the TSK-type output. A self-constructing learning algorithm, which consists of the self-clustering method (SCM) and the backpropagation algorithm, is proposed. The proposed the SCM scheme is a fast, one-pass algorithm for a dynamic estimation of the number of hypercube cells in an input data space. The clustering technique does not require prior knowledge of things such as the number of clusters present in a data set. The backpropagation algorithm is used to tune the adjustable parameters. Illustrative examples were conducted to show the performance and applicability of the proposed model.