Image compression using principal component neural networks

Principal component analysis (PCA) is a well-known statistical processing technique that allows to study the correlations among the components of multivariate data and to reduce redundancy by projecting the data over a proper basis. The PCA may be performed both in a batch and in a recursive fashion; the latter method has been proven to be very effective in presence of high dimension data, as in image compression. The aim of this paper is to present a comparison of principal component neural networks for still image compression and coding. We first recall basic concepts related to neural PCA, then we recall from the scientific literature a number of principal component networks, and present comparisons about the structures, the learning algorithms and the required computational efforts, along with a discussion of the advantages and drawbacks related to each technique. The conclusion of our wide comparison among eight principal component networks is that the cascade recursive least-squares algorithm by Cichocki, Kasprzak and Skarbek exhibits the best numerical and structural properties.     

Finger-vein pattern identification using principal component analysis and the neural network technique

This paper presents a personal identification system using finger-vein patterns with component analysis and neural network technology. In the proposed system, the finger-vein patterns are captured by a device that can transmit near infrared through the finger and record the patterns for signal analysis. The proposed biometric system for verification consists of a combination of feature extraction using principal component analysis (PCA) and pattern classification using back-propagation (BP) network and adaptive neuro-fuzzy inference system (ANFIS). Finger-vein features are first extracted by PCA method to reduce the computational burden and removes noise residing in the discarded dimensions. The features are then used in pattern classification and identification. To verify the effect of the proposed ANFIS in the pattern classification, the BP network is compared with the proposed system. The experimental results indicated the proposed system using ANFIS has better performance than the BP network for personal identification using the finger-vein patterns.     

An adaptive neural networks formulation for the two-dimensional principal component analysis

This study, for the first time, developed an adaptive neural networks (NNs) formulation for the two-dimensional principal component analysis (2DPCA), whose space complexity is far lower than that of its statistical version. Unlike the NNs formulation of principal component analysis (PCA, i.e., 1DPCA), the solution with lower iteration in nature aims to directly deal with original image matrices. We also put forward the consistence in the conceptions of ‘eigenfaces’ or ‘eigengaits’ in both 1DPCA and 2DPCA neural networks. To evaluate the performance of the proposed NN, the experiments were carried out on AR face database and on 64 × 64 pixels gait energy images on CASIA(B) gait database. The less reconstruction error was exploited using the proposed NN in the condition of a large sample set compared to adaptive estimation of learning algorithms for NNs of PCA. On the contrary, if the sample set was small, the proposed NN could achieve a higher residue error than PCA NNs. The amount of calculation for the proposed NN here could be smaller than that for the PCA NNs on the feature extraction of the same image matrix, which represented an efficient solution to the problem of training images directly. On face and gait recognition tasks, a simple nearest neighbor classifier test indicated a particular benefit of the neural network developed here which serves as an efficient alternative to conventional PCA NNs.     

Nonlinear systems identification using dynamic multi-time scale neural networks

In this paper, two Neural Network (NN) identifiers are proposed for nonlinear systems identification via dynamic neural networks with different time scales including both fast and slow phenomena. The first NN identifier uses the output signals from the actual system for the system identification. The on-line update laws for dynamic neural networks have been developed using the Lyapunov function and singularly perturbed techniques. In the second NN identifier, all the output signals from nonlinear system are replaced with the state variables of the neuron networks. The on-line identification algorithm with dead-zone function is proposed to improve nonlinear system identification performance. Compared with other dynamic neural network identification methods, the proposed identification methods exhibit improved identification performance. Three examples are given to demonstrate the effectiveness of the theoretical results.     

Nonlinear dynamic system identification using Chebyshev functionallink artificial neural networks

A computationally efficient artificial neural network (ANN) for the purpose of dynamic nonlinear system identification is proposed. The major drawback of feedforward neural networks, such as multilayer perceptrons (MLPs) trained with the backpropagation (BP) algorithm, is that they require a large amount of computation for learning. We propose a single-layer functional-link ANN (FLANN) in which the need for a hidden layer is eliminated by expanding the input pattern by Chebyshev polynomials. The novelty of this network is that it requires much less computation than that of a MLP. We have shown its effectiveness in the problem of nonlinear dynamic system identification. In the presence of additive Gaussian noise, the performance of the proposed network is found to be similar or superior to that of a MLP. A performance comparison in terms of computational complexity has also been carried out.     

Nonlinear system identification using memetic differential evolution trained neural networks

Several gradient-based approaches such as back propagation (BP) and Levenberg Marquardt (LM) methods have been developed for training the neural network (NN) based systems. But, for multimodal cost functions these procedures may lead to local minima, therefore, the evolutionary algorithms (EAs) based procedures are considered as promising alternatives. In this paper we focus on a memetic algorithm based approach for training the multilayer perceptron NN applied to nonlinear system identification. The proposed memetic algorithm is an alternative to gradient search methods, such as back-propagation and back-propagation with momentum which has inherent limitations of many local optima. Here we have proposed the identification of a nonlinear system using memetic differential evolution (DE) algorithm and compared the results with other six algorithms such as Back-propagation (BP), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Genetic Algorithm Back-propagation (GABP), Particle Swarm Optimization combined with Back-propagation (PSOBP). In the proposed system identification scheme, we have exploited DE to be hybridized with the back propagation algorithm, i.e. differential evolution back-propagation (DEBP) where the local search BP algorithm is used as an operator to DE. These algorithms have been tested on a standard benchmark problem for nonlinear system identification to prove their efficacy. First examples shows the comparison of different algorithms which proves that the proposed DEBP is having better identification capability in comparison to other. In example 2 good behavior of the identification method is tested on an one degree of freedom (1DOF) experimental aerodynamic test rig, a twin rotor multi-input-multi-output system (TRMS), finally it is applied to Box and Jenkins Gas furnace benchmark identification problem and its efficacy has been tested through correlation analysis.     

主成分分析与神经网络的结合在多变量序列预测中的应用

目前预测方法的研究主要集中在单变量时间序列上,本文建立起一种针对多元变量非线性时间序列建模和预测的方法框架.首先,同时考虑序列状态间的线性相关性和非线性相关性,建立初始延迟窗以包含充分的预测信息;然后,利用主成分分析(PCA)方法寻找不同变量在数据空间中的最大方差方向,扩展PCA应用于提取多个变量的综合信息,重构多元变量输入状态相空间;最后,利用神经网络逼近不同变量之间以及当前状态和将来状态之间的函数映射关系,实现多元变量预测.对Ro¨ssler混沌方程和大连降雨、气温序列的预测仿真说明了本文方法的有效性,为多元变量时间序列分析提供了一条新的途径.     

Visualization of learning in multilayer perceptron networks using principal component analysis

This paper is concerned with the use of scientific visualization methods for the analysis of feedforward neural networks (NNs). Inevitably, the kinds of data associated with the design and implementation of neural networks are of very high dimensionality, presenting a major challenge for visualization. A method is described using the well-known statistical technique of principal component analysis (PCA). This is found to be an effective and useful method of visualizing the learning trajectories of many learning algorithms such as backpropagation and can also be used to provide insight into the learning process and the nature of the error surface.     

Online identification of nonlinear system using reduced kernel principal component analysis

The Principal Component Analysis (PCA) is a powerful technique for extracting structure from possibly high-dimensional data sets. It is readily performed by solving an eigenvalue problem, or by using iterative algorithms that estimate principal components. This paper proposes a new method for online identification of a nonlinear system modelled on Reproducing Kernel Hilbert Space (RKHS). Therefore, the PCA technique is tuned twice, first we exploit the Kernel PCA (KPCA) which is a nonlinear extension of the PCA to RKHS as it transforms the input data by a nonlinear mapping into a high-dimensional feature space to which the PCA is performed. Second, we use the Reduced Kernel Principal Component Analysis (RKPCA) to update the principal components that represent the observations selected by the KPCA method.     

Principal component analysis of fuzzy data using autoassociative neural networks

This paper describes an extension of principal component analysis (PCA) allowing the extraction of a limited number of relevant features from high-dimensional fuzzy data. Our approach exploits the ability of linear autoassociative neural networks to perform information compression in just the same way as PCA, without explicit matrix diagonalization. Fuzzy input values are propagated through the network using fuzzy arithmetics, and the weights are adjusted to minimize a suitable error criterion, the inputs being taken as target outputs. The concept of correlation coefficient is extended to fuzzy numbers, allowing the interpretation of the new features in terms of the original variables. Experiments with artificial and real sensory evaluation data demonstrate the ability of our method to provide concise representations of complex fuzzy data.     

PCA与PCNN结合的遥感图像分割方法

提出了一种基于主分量分析（PCA）与脉冲耦合神经网络（PCNN）结合的遥感图像分割方法。通过对图像在每个像素的邻域的基础上进行主分量分析,产生每个图像像素的特征向量,再用PCNN对得到的特征图像进行点火分割。实验结果表明,与传统方法比较,该算法在分割结果、实时性以及稳健性方面具有较强的优越性。     

Process identification based on last principal component analysis

A simple linear identification algorithm is presented in this paper. The last principal component (LPC), the eigenvector corresponding to the smallest eigenvalue of a non-negative symmetric matrix, contains an optimal linear relation of the column vectors of the data matrix. This traditional, well-known principal component analysis is extended to the generalized last principal component analysis (GLPC). For processes with colored measurement noise or disturbances, consistency of the GLPC estimator is achieved without involving iteration or non-linear numerical optimization. The proposed algorithm is illustrated by a simulated example and application to a pilot-scale process.     

Nonlinear system identification using wavelet networks

A novel identification scheme using wavelet networks is presented for nonlinear dynamical systems. Based on fixed wavelet networks, parameter adaptation laws are developed using a Lyapunov synthesis approach. This guarantees the stability of the overall identification scheme and the convergence of both the parameters and the state errors, even in the presence of modelling errors. Using the decomposition and reconstruction techniques of multiresolution decompositions, variable wavelet networks are introduced to achieve a desired estimation accuracy and a suitable sized network, and to adapt to variations of the characteristics and operating points in nonlinear systems. B-spline wavelets are used to form the wavelet networks and the identification scheme is illustrated using a simulated example.     

Object detection using pulse coupled neural networks

Describes an object detection system based on pulse coupled neural networks. The system is designed and implemented to illustrate the power, flexibility and potential the pulse coupled neural networks have in real-time image processing. In the preprocessing stage, a pulse coupled neural network suppresses noise by smoothing the input image. In the segmentation stage, a second pulse coupled neural-network iteratively segments the input image. During each iteration, with the help of a control module, the segmentation network deletes regions that do not satisfy the retention criteria from further processing and produces an improved segmentation of the retained image. In the final stage each group of connected regions that satisfies the detection criteria is identified as an instance of the object of interest.     

Parametric system identification using neural networks

Neural networks are used in many applications such as image recognition, classification, control and system identification. However, the parameters of the identified system are embedded within the neural network architecture and are not identified explicitly. In this paper, a mathematical relationship between the network weights and the transfer function parameters is derived. Furthermore, an easy-to-follow algorithm that can estimate the transfer function models for multi-layer feedforward neural networks is proposed. These estimated models provide an insight into the system dynamics, where information such as time response, frequency response, and pole/zero locations can be calculated and analyzed. In order to validate the suitability and accuracy of the proposed algorithm, four different simulation examples are provided and analyzed for three-layer neural network models.     

Botnet traffic identification using neural networks

Advancement of information and communication techniques have led to share big amount of information which is increasing day by day through online activities and creating new added value over the internet services. At the same time threats to the security of cyber world has been increased with increasing number of heterogeneous connection points having powerful computational capacity. Internet being used to interact and control such automatic network devices connected to it. But hackers/crackers can exploit this network environment by putting malicious dummy node(s) or machine(s) called Botnet(s) to co-ordinate the attacks on security such as Denial of Service (DoS) or Distributed Denial of Service (DDoS). The proposed method attempts to identify those mallicious Botnet traffic from regular traffic using novel deep learning approaches like Artificial Neural Networks (ANN), Gatted Recurrent Units (GRU), Long or Short Term Memory (LSTM) model. The proposed model demonstrates significant improvement of all previous works. The testing dataset, Bot-IoT dataset is the latest and one of the largest public domain dataset used to justify improvement. Testing shows 99.7% classification accuracy which is precise and better than all previous works done. Results analysis and comparison shows the accuracy and supremacy over the latest work done on this field.

     

Fuzzy auto-associative neural networks for principal component extraction of noisy data.

In this paper, we propose a fuzzy auto-associative neural network for principal component extraction. The objective function is based on reconstructing the inputs from the corresponding outputs of the auto-associative neural network. Unlike the traditional approaches, the proposed criterion is a fuzzy mean squared error. We prove that the proposed objective function is an appropriate fuzzy formulation of auto-associative neural network for principal component extraction. Simulations are given to show the performances of the proposed neural networks in comparison with the existing method.     

Spatial pattern of a seismo-ionospheric signature using principal component analysis

This article uses principal component analysis (PCA) to determine the spatial pattern of total electron content (TEC) anomalies in the ionosphere corresponding to China's Wenchuan Earthquake of 12 May 2008 UTC (M_w?=?7.9). PCA is applied to global ionospheric maps (GIMs) with transforms conducted for the time period from 08:00 to 10:00 UT on 9 May 2008. The GIMs are subdivided into 100 smaller maps (36° longitude and 18° latitude). The smaller maps (71?×?71 pixels) form the transform matrices of corresponding dimensions (2?×?1) through image processing. The transform allows for extreme principal eigenvalues to be assigned to the seismo-ionospheric signature described by Zhao et al. [2008 Zhao, B., Yu, T., Wang, M., Wan, W., Lei, J., Liu, L. and Ning, B. 2008. Is an unusual large enhancement of ionospheric electron density linked with the 2008 great Wenchuan earthquake?. Journal of Geophysical Research, 113: A11304 doi:doi: 10.1029/2008JA013613[Crossref], [Web of Science ®] , [Google Scholar], Is an unusual large enhancement of ionospheric electron density linked with the 2008 great Wenchuan earthquake? Journal of Geophysical Research, 113, A11304, doi: 10.1029/2008JA013613.]. Results show that the range of the TEC anomaly declines with height; however, the anomaly becomes more localized and intense at higher altitudes (250–300 km), giving the anomaly the spatial pattern of a downward-facing trumpet.     

A system to diagnose atherosclerosis via wavelet transforms,principal component analysis and artificial neural networks

In this study, Doppler ultrasound signals were acquired from carotid arteries of 82 patients with atherosclerosis and 95 healthy volunteers. We have employed discrete wave transform (DWT) of Doppler signals and power spectral density graphics of these decomposed signals using Welch method. After that, we have performed Principal component analysis (PCA) for data reduction and ANN in order to distinguish between atherosclerosis and healthy subjects.After the training phase, testing of the artificial neural network (ANN) was established. The overall results show that 97.9% correct classification was achieved, whereas two false classifications have been observed for the test group of 97 people.In conclusion we are proposing a complimentary expert system that can be coupled to software of the ultrasonic Doppler devices. The diagnosis performances of this study show the advantages of this system: it is rapid, easy to operate, noninvasive, inexpensive and making a decision without hesitation.     

高效液相色谱结合主成分分析研究人参不同部位的差异性

采用反相高效液相色谱法系统地对人参芦头、主根的木栓部、韧皮部和木质部、参须等部位进行了分离分析,并利用主成分分析对HPLC数据进行了分类研究,探索了人参根不同部位和不同位点的差异性.从木栓部、韧皮部至木质部人参皂苷和其它组分峰高不断降低,芦头和参须组分和峰高与木栓部类似;木栓部、韧皮部和木质部不同位点组分和含量大致是芦头到须根逐渐减少;木栓部和木质部组成有差异性,韧皮部数据落在两者之间.本研究通过高效液相色谱和主成分分析清晰的揭示了人参根不同部位和不同位点的规律性和差异性.