粗糙BP网络在葡萄病害分类中的应用

粗糙集无需提供问题所需处理的数据集合之外的任何先验信息,是一种通过知识约简,消除冗余数据的软计算方法;BP神经网络是一种通过自身的学习机制自动形成所要求的决策区域技术.综合了粗糙集和BP神经网络的各自优势,构建了一种新颖的葡萄病害分类模型.测试结果表明,所建模型对葡萄病害分类是行之有效的.     

Medical image compression using topology-preserving neural networks

A novel method based on topology-preserving neural networks is used to implement vector quantization for medical image compression. The described method is an innovative image compression procedure, which differentiates itself from known systems in several ways. It can be applied to larger image blocks and represents better probability distribution estimation methods. A transformation-based operation is applied as part of the encoder on the block-decomposed image. The quantization process is performed by a “neural-gas” network which applied to vector quantization converges quickly to low distortion errors and reaches a distortion error lower than that resulting from Kohonen's feature map or the LBG algorithm. To study the efficiency of our algorithm, we blended mathematical phantom features into clinically proved cancer free mammograms. The influence of the neural compression method on the phantom features and the mammo-graphic image is not visually perceptible up to a high compression rate.     

Satellite image classification using granular neural networks

The increased synergy between neural networks (NN) and fuzzy sets has led to the introduction of granular neural networks (GNNs) that operate on granules of information, rather than information itself. The fact that processing is done on a conceptual rather than on a numerical level, combined with the representation of granules using linguistic terms, results in increased interpretability. This is the actual benefit, and not increased accuracy, gained by GNNs. The constraints used to implement the GNN are such that accuracy degradation should not be surprising. Having said that, it is well known that simple structured NNs tend to be less prone to over‐fitting the training data set, maintaining the ability to generalize and more accurately classify previously unseen data. Standard NNs are frequently found to be accurate but difficult to explain, hence they are often associated with the black box syndrome. Because in GNNs the operation is carried out at a conceptual level, the components have unambiguous meaning, revealing how classification decisions are formed. In this paper, the interpretability of GNNs is exploited using a satellite image classification problem. We examine how land use classification using both spectral and non‐spectral information is expressed in GNN terms. One further contribution of this paper is the use of specific symbolization of the network components to easily establish causality relationships.     

高速公路动态交通流的BP神经网络建模

通过对高速公路宏观动态交通流模型的分析,针对高速公路交通系统的非线性时变特点,应用BP神经网络建立了高速公路宏观动态交通流模型。并利用一段高速公路的交通流数据对BP神经网络进行训练,得到网络参数。最后,为了验证BP网络模型的有效性,在MATLAB环境中对模型进行了仿真,并将仿真结果与原始模型的结果进行了比较。结果表明,该方法能较准确地描述高速公路交通流的真实行为,并且能够适应交通状况的变化。     

Bayesian MLP neural networks for image analysis

We demonstrate the advantages of using Bayesian multi-layer perceptron (MLP) neural networks for image analysis. The Bayesian approach provides consistent way to do inference by combining the evidence from the data to prior knowledge from the problem. A practical problem with MLPs is to select the correct complexity for the model, i.e., the right number of hidden units or correct regularization parameters. The Bayesian approach offers efficient tools for avoiding overfitting even with very complex models, and facilitates estimation of the confidence intervals of the results. In this contribution we review the Bayesian methods for MLPs and present comparison results from two case studies. In the first case, MLPs were used to solve the inverse problem in electrical impedance tomography. The Bayesian MLP provided consistently better results than other methods. In the second case, the goal was to locate trunks of trees in forest scenes. With Bayesian MLP it was possible to use large number of potentially useful features and prior for determining the relevance of the features automatically.     

Situation recognition using image moments and recurrent neural networks

Surveillance is an important need for a secured and supervised environment. Manual supervision for the purpose of surveillance proves to be expensive and prone to slipups. Many researchers have worked to provide an automated solution to this problem. In this article, we present a solution to this problem using image moments and recurrent neural networks. For this purpose, frames are first extracted from a live video and the foreground of the frame is sieved out while the background is discarded. Feature vectors are obtained for each frame after computing raw, central, scale-invariant and rotation-invariant moments of the images. These vectors are used to train and ultimately simulate a recurrent neural network. The results generated from this model exhibit an accuracy of 96.4 % in identification of events within consecutive frames.     

Perfect image segmentation using pulse coupled neural networks

This paper describes a method for segmenting digital images using pulse coupled neural networks (PCNN). The pulse coupled neuron (PCN) model used in PCNN is a modification of the cortical neuron model of Eckhorn et al. (1990). A single layered laterally connected PCNN is capable of perfectly segmenting digital images even when there is a considerable overlap in the intensity ranges of adjacent regions. Conditions for perfect image segmentation are derived. It is also shown that addition of an inhibition receptive field to the neuron model increases the possibility of perfect segmentation. The inhibition input reduces the overlap of intensity ranges of adjacent regions by effectively compressing the intensity range of each region.     

Convergence analysis of BP neural networks via sparse response regularization

Backpropagation (BP) algorithm is the typical strategy to train the feedforward neural networks (FNNs). Gradient descent approach is the popular numerical optimal method which is employed to implement the BP algorithm. However, this technique frequently leads to poor generalization and slow convergence. Inspired by the sparse response character of human neuron system, several sparse-response BP algorithms were developed which effectively improve the generalization performance. The essential idea is to impose the responses of hidden layer as a specific L₁ penalty term on the standard error function of FNNs. In this paper, we mainly focus on the two remaining challenging tasks: one is to solve the non-differential problem of the L₁ penalty term by introducing smooth approximation functions. The other aspect is to provide a rigorous convergence analysis for this novel sparse response BP algorithm. In addition, an illustrative numerical simulation has been done to support the theoretical statement.     

小波和神经网络在心音识别中的应用

提出一种心音的特征提取和分类方法,用离散小波变换分解、重构产生信号的细节包络,进而用于提取特征,从预处理的信号中提取统计特性,作为心音分类的特征。多层感知器用于心音的分类,并通过250个心动周期得到验证,算法识别率达到92%。     

Classification of remotely-sensed image data using artificial neural networks

Abstract

Abstract. Artificial neural networks have been used recently for speech and character recognition. Their application for the classification of remotely-sensed images is reported in this Letter. Remotely sensed image data are usually large in size and spectral overlaps among classes of ground objects are common. This results in low convergence performance of the Back-Propagation Algorithm in a neural network classifier. A Blocked Back-Propagation (BB-P) algorithm was proposed arid described in this Letter. It improved convergence performance and classification accuracy.     

BP神经网络模型的优化

文中综述了ＢＰ神经网络模型的一些优化方法,并对这些方法进行了分析,最后提出了存在的问题和进一步的工作。     

Scenery image recognition and interpretation using fuzzy inference neural networks

In this paper, we propose a new image recognition and interpretation system. The proposed system is composed of three processes: (1) regional segmentation process; (2) image recognition process; and (3) image interpretation process. As a pre-processing in the regional segmentation process, an input image is divided into some proper regions using techniques based on K-means algorithm. In both the image recognition and the interpretation processes, fuzzy inference neural networks (FINNs) working in parallel are employed to achieve a high level of recognition and interpretation. Scenery images are used and it is confirmed that the system has an average of 71.9% accuracy rate in the recognition process and good results in the interpretation process without heuristic knowledge. In addition, it is also confirmed that the proposed system has an ability to extract proper rules for the image recognition and interpretation.     

Color image segmentation using feedforward neural networks with FCM

This paper proposes a hybrid technique for color image segmentation. First an input image is converted to the image of CIE L*a*b* color space. The color features “a” and “b” of CIE L*a*b* are then fed into fuzzy C-means (FCM) clustering which is an unsupervised method. The labels obtained from the clustering method FCM are used as a target of the supervised feed forward neural network. The network is trained by the Levenberg-Marquardt back-propagation algorithm, and evaluates its performance using mean square error and regression analysis. The main issues of clustering methods are determining the number of clusters and cluster validity measures. This paper presents a method namely co-occurrence matrix based algorithm for finding the number of clusters and silhouette index values that are used for cluster validation. The proposed method is tested on various color images obtained from the Berkeley database. The segmentation results from the proposed method are validated and the classification accuracy is evaluated by the parameters sensitivity, specificity, and accuracy.     

基于BP神经网络和中值滤波的ECT图像重构算法

电容层析成像技术（ECT-Electrical Capacitance Tomography）是基于电容敏感原理的过程成像技术,具有非侵入性、造价低、安装方便、实时性好等优点。图像重构作为ECT系统的关键技术,其实质是根据物体内部介电常数的空间分布推导出管道中各相分布的过程。本文针对重构问题的非线性、病态性等特点,采用了基于BP神经网络的ECT图像重构算法,并引入中值滤波对重构图像进行增强。仿真结果表明,该算法可以有效地实现图像重构和令人满意的增强效果,它大大提高了重建图像的质量,是一种有效的ECT图像重构算法。     

A robust image watermarking scheme using Arnold transform and BP neural network

Neural Computing and Applications - The protection and authentication of multimedia contents and copyright have become a great concern in the fast-growing Internet environment. This paper presents...     

The adaptive control using BP neural networks for a nonlinear servo-motor

The servo-motor possesses a strongly nonlinear property due to the effect of the stimulating input voltage, load-torque and environmental operating conditions. So it is rather difficult to derive a traditional mathematical model which is capable of expressing both its dynamics and steady-state characteristics. A neural network-based adaptive control strategy is proposed in this paper. In this method, two neural networks have been adopted for system identification （NNI） and control （NNC）, respectively. Then, the commonly-used specialized learning has been modified, by taking the NNI output as the approximation output of the servo-motor during the weights training to get sensitivity information. Moreover, the rule for choosing the learning rate is given on the basis of the analysis of Lyapunov stability. Finally, an example of applying the proposed control strategy on a servo-motor is presented to show its effectiveness.     

A board system for high-speed image analysis and neural networks

Two ANNA neural-network chips are integrated on a 6U VME board, to serve as a high-speed platform for a wide variety of algorithms used in neural-network applications as well as in image analysis. The system can implement neural networks of variable sizes and architectures, but can also be used for filtering and feature extraction tasks that are based on convolutions. The board contains a controller implemented with field programmable gate arrays (FPGA's), memory, and bus interfaces, all designed to support the high compute power of the ANNA chips. This new system is designed for maximum speed and is roughly 10 times faster than a previous board. The system has been tested for such tasks as text location, character recognition, and noise removal as well as for emulating cellular neural networks (CNN's). A sustained speed of up to two billion connections per second (GC/s) and a recognition speed of 1000 characters per second has been measured.     

Identification of nonlinear dynamical systems using multilayered neural networks

A novel multilayer discrete-time neural net paradigm is presented for the identification of multi-input multi-output (MIMO) nonlinear dynamical systems. The major novelty of this approach is a rigorous proof of identification error convergence that reveals a requirement for a new identifier structure and nonstandard weight tuning algorithms. The NN identifier includes modified delta rule weight tuning and exhibits a learning-while-functioning feature instead of learning-then-functioning, so that the identification is on-line with no explicit off-line learning phase needed. The structure of the neural net (NN) identifier is derived using a passivity aproach. Linearity in the parameters is not required and certainty equivalence is not used. The notion of persistency of excitation (PE) and passivity properties of the multilayer NN are defined and used in the convergence analysis of both the identification error and the weight estimates.     

基于神经网络的垃圾焚烧炉过程控制

人工操作排除垃圾焚烧炉故障对操作员要求较高,且自动化程度低.应用BP神经网络方法,采用madab软件编程建立垃圾焚烧炉过程控制模型,对垃圾焚烧炉两种典型故障的排除进行研究.在过程控制模型的建立过程中,采用神经网络集成,提高神经网络模型的泛化能力.最后以49组实际工况数据作为检验样本,检验误差率为7.612%和6.429%.检验结果表明神经网络集成可以提高模型的计算精度,该模型可以用于垃圾焚烧炉过程控制,提高设备的自动化程度.     

Context-aware saliency detection for image retargeting using convolutional neural networks

Multimedia Tools and Applications - Image retargeting is the task of making images capable of being displayed on screens with different sizes. This work should be done so that high-level visual...