A vector neural network for emitter identification

This paper proposes a three-layer vector neural network (VNN) with a supervised learning algorithm suitable for signal classification in general, and for emitter identification (EID) in particular. The VNN can accept interval-value input data as well as scalar input data. The input features of the EID problems include the radio frequency, pulse width, and pulse repetition interval of a received emitter signal. Since the values of these features vary in interval ranges in accordance with a specific radar emitter, the VNN is proposed to process interval-value data in the EID problem. In the training phase, the interval values of the three features are presented to the input nodes of VNN. A new vector-type backpropagation learning algorithm is derived from an error function defined by the VNN's actual output and the desired output indicating the correct emitter type of the corresponding feature intervals. The algorithm can tune the weights of VNN optimally to approximate the nonlinear mapping between a given training set of feature intervals and the corresponding set of desired emitter types. After training, the VNN can be used to identify the sensed scalar-value features from a real-time received emitter signal. A number of simulations are presented to demonstrate the effectiveness and identification capability of VNN, including the two-EID problem and the multi-EID problem with/without additive noise. The simulated results show that the proposed algorithm cannot only accelerate the convergence speed, but it can help avoid getting stuck in bad local minima and achieve higher classification rate.     

A dynamic learning neural network for remote sensing applications

The neural network learning process is to adjust the network weights to adapt the selected training data. Based on the polynomial basis function (PBF) modeled neural network that is a modified multilayer perceptrons (MLP) network, a dynamic learning algorithm (DL) is proposed. The presented learning algorithm makes use of the Kalman filtering technique to update the network weights, in the sense that the stochastic characteristics of incoming data sets are implicitly incorporated into the network. The Kalman gains which represent the learning rates of the network weights updating are calculated by using the U-D factorization. By concatenating all of the network weights at each layer to form a long vector such that it can be updated without propagating back, the proposed algorithm improves the performance of convergence to which the backpropagation (BP) learning algorithm often suffers. Numerical illustrations are carried out using two categories of problems: multispectral imagery classification and surface parameters inversion. Results indicates the use of Kalman filtering algorithm not only substantially increases the convergence rate in the learning stage, but also enhances the separability for highly nonlinear boundaries problems, as compared to BP algorithm, suggesting that the proposed DL neural network provides a practical and potential tool for remote sensing applications     

基于神经网络的模数转换电路动态误差源识别系统设计

为提升信号识别电路的电量采集精度,实现理想状态下的电力误差校准,设计基于神经网络的模数转换电路动态误差源识别系统。以CNN神经网络作为模数转换电路的物理依赖环境,通过合理选取动态识别元件的方式,实现误差源识别系统的硬件运行环境搭建。在此基础上,将模拟电流转化成数字信号,再将其完整存储于系统数据库中,利用既定数学运算公式对已存储的数字信号进行识别精度提纯处理,实现误差源识别系统的软件运行环境搭建,联合相关硬件执行设备,完成基于神经网络的模数转换电路动态误差源识别系统设计。实际应用结果表明,在加压环境下,新型误差源识别系统的电量采集精度达到90%,单位时间内的信号识别量超过7.5×109TB,理想状态下信号识别电路的电力误差校准能力得到有效保障。     

动态泛回归神经网络预测的研究与仿真

在HUATECA3000过程控制实验系统上选取实验对象,研究了三容水箱液位非线性控制系统,提出了一种动态泛回归神经网络预测控制算法.先后通过开环与闭环控制,搭建了SIMUUNK仿真模型研究算法的有效性.仿真结果表明闭环预测控制改进了系统在干扰作用下的稳态和动态性能.     

A novel traffic identification approach based on multifractal analysis and combined neural network

An accurate identification of Internet traffic of different applications is highly relevant for a broad range of network management and measurement tasks, including traffic engineering, service differentiation, performance monitoring, and security. Traditional traffic identification approaches have become increasingly inaccurate due to restrictions of port numbers, protocol signatures, traffic encryption, and etc. In this paper, a new traffic identification approach based on multifractal analysis of wavelet energy spectrum and classification of combined neural network models is proposed. The proposed approach is able to achieve the identification of different Internet application traffic by performing classification over the wavelet energy spectrum coefficients that were inferred from the original traffic. Without using any payload information, the proposed approach has more advantages over traditional methods. The experiment results illustrate that the proposed approach has satisfactory identification results.     

基于模糊神经网络的目标识别

结合模糊推理和神经网络两种方法的优点,从网络的结构、工作过程、学习算法等方面,探讨了一种基于模糊神经网络(FNN)的目标识别方法。通过仿真结果证明,此方法确实可行。     

基于动态BP神经网络的财务危机预警算法研究

为进一步提升模型合理性和预测结果准确度,充分考虑公司财务情况历史值的影响,通过对不同时期的财务面板数据赋以不同权重,设计提出了一种基于Logit-动态BP神经网络的财务危机预警机制。实证结果显示,基于面板数据的新模型能更好地体现财务危机的发生机理,因而具备良好预警精度;在对财务危机公司及财务正常公司预警实验中,其预测性能均优于现有Logit回归分析模型和传统神经网络模型。     

Two-stage neural network architecture for feedback control of dynamic systems

In this paper we investigate system identification and the design of a controller using neural networks. A two-stage neural network design for controllers using single-layer structures with functional enhancements is introduced. This neural network architecture allows the design of a controller with less a priori knowledge about the plant as well as allowing for nonlinear plants. The paper also addresses the special characteristics and problems concerning the use of neural networks in control and demonstrates their performance by showing the successful implementation of a nonlinear control example via simulation.Christop Müller-Dott was a Fulbright Scholar with the Department of Electrical Engineering.     

复数Hopfield神经网络盲均衡QAM信号

为了解决复数多值信号的盲均衡问题,本文提出了基于复数Hopfield神经网络盲均衡多值信号的方法:将基于Hopfield神经网络的盲均衡算法从实数域推广到复数域.在复数域成功构造了复数Hopfield神经网络,重点针对16QAM信号进行盲均衡.并验证了此系统可以处理非统计量字符,即处理16QAM信号的Hopfield神...     

GRAIL: a multi-agent neural network system for gene identification

Identifying genes within large regions of uncharacterized DNA is a difficult undertaking and is currently the focus of many research efforts. We describe a gene localization and modeling system, called GRAIL. GRAIL is a multiple sensor-neural network-based system. It localizes genes in anonymous DNA sequence by recognizing features related to protein-coding regions and the boundaries of coding regions, and then combines the recognized features using a neural network system. Localized coding regions are then “optimally” parsed into a gene model. Through years of extensive testing GRAIL consistently achieves about 90% of coding portions of test genes with a false positive rate of about 10% A number of genes for major genetic diseases have been located through the use of GRAIL, and over 1000 research laboratories worldwide use GRAIL on regular bases for localization of genes on their newly sequenced DNA     

Speaker identification for security systems usingreinforcement-trained pRAM neural network architectures

Speaker identification may be employed as part of a security system requiring user authentication. In this case, the claimed identity of the user is known from a magnetic card and PIN number, for example, and an utterance is requested to confirm the identity of the user. A fast response is necessary in the confirmation phase and a fast registration process for new users is desirable. The time encoded signal processing and recognition (TESPAR) digital language is used to preprocess the speech signal. A speaker cannot be identified directly from the single TESPAR vector since there is a highly nonlinear relationship between the vector's components such that vectors are not linearly separable. Therefore the vector and its characteristics suggest that classification using a neural network will provide an effective solution. Good classification performance has been achieved using a probabilistic RAM (pRAM) neuron. Four probabilistic pRAM neural network architectures are presented. A performance of approximately 97% correct classifications has been obtained, which is similar to results obtained elsewhere (M. Sharma and R.J. Mammone, 1996), and slightly better than a MLP network. No speech recognition stage was used in obtaining these results, so the performance relates only to identifying a speaker's voice and is therefore independent of the spoken phrase. This has been achieved in a hardware-realizable system which may be incorporated into a smart-card or similar application     

A reconfigurable VLSI neural network

Due to the variety of architectures that need be considered while attempting solutions to various problems using neural networks, the implementation of a neural network with programmable topology and programmable weights has been undertaken. A new circuit block, the distributed neuron-synapse, has been used to implement a 1024 synapse reconfigurable network on a VLSI chip. In order to evaluate the performance of the VLSI chip, a complete test setup consisting of hardware for configuring the chip, programming the synaptic weights, presenting analog input vectors to the chip, and recording the outputs of the chip, has been built. Following the performance verification of each circuit block on the chip, various sample problems were solved. In each of the problems the synaptic weights were determined by training the neural network using a gradient-based learning algorithm which is incorporated in the experimental test setup. The results of this work indicate that reconfigurable neural networks built using distributed neuron synapses can be used to solve various problems efficiently     

A neural network approach-decision neural network (DNN) for preference assessment

A new neural-network-based approach to assess the preference of a decision-maker (DM) for the multiple objective decision making (MODM) problem is presented in this paper. A new neural network structure with a "twin-topology" is introduced in this approach. We call this neural network a decision neural network (DNN). The characteristics of the DNN are discussed, and the training algorithm for DNN is presented as well. The DNN enables the decision-maker to make pairwise comparisons between different alternatives, and these comparison results are used as learning samples to train the DNN. The DNN is applicable for both accurate and inaccurate comparisons (results are given in approximate values or interval scales). The performance of the DNN is evaluated with several typical forms of utility functions. Results show that DNN is an effective and efficient way for modeling the preference of a decision-maker.     

Application of functional link neural network to HVAC thermaldynamic system identification

Recent efforts to incorporate aspects of artificial intelligence into the design and operation of automatic control systems have focused attention on techniques such as fuzzy logic, artificial neural networks and expert systems. The use of computers for direct digital control highlights the recent trend toward more effective and efficient heating, ventilating and air-conditioning (HVAC) control methodologies. Researchers in the HVAC field have stressed the importance of self-learning in building control systems and have encouraged further studies in the integration of optimal control and other advanced techniques into the formulation of such systems. Artificial neural networks can also be used to emulate the plant dynamics, in order to estimate future plant outputs and obtain plant input/output sensitivity information for online neural control adaptation. This paper describes a functional link neural network approach to performing the HVAC thermal dynamic system identification. Methodologies to reduce inputs of the functional link network to reduce the complexity and speed up the training speed are presented. Analysis and comparison between the functional link network approach and the conventional network approach for the HVAC thermal modeling are also presented     

System identification and modelling based on a double modified multi-valued neural network

A novel identification technique for the extraction of lumped circuit models of general distributed or stray devices is presented. The approach is based on two multi-valued neuron neural networks used in a joined architecture able to extract hidden parameters. The convergence allows the validation of the approximated lumped model and the extraction of the correct values. The inputs of the neural network are the geometrical parameters of a given structure, while the outputs represent the estimation of the lumped circuit parameters. The method uses a frequency response analysis approach in order to elaborate the data to present to the net.     

Super-resolution target identification from remotely sensed imagesusing a Hopfield neural network

Fuzzy classification techniques have been developed recently to estimate the class composition of image pixels, but their output provides no indication of how these classes are distributed spatially within the instantaneous field of view represented by the pixel. As such, while the accuracy of land cover target identification has been improved using fuzzy classification, it remains for robust techniques that provide better spatial representation of land cover to be developed. Such techniques could provide more accurate land cover metrics for determining social or environmental policy, for example. The use of a Hopfield neural network to map the spatial distribution of classes more reliably using prior information of pixel composition determined from fuzzy classification was investigated. An approach was adopted that used the output from a fuzzy classification to constrain a Hopfield neural network formulated as an energy minimization tool. The network converges to a minimum of an energy function, defined as a goal and several constraints. Extracting the spatial distribution of target class components within each pixel was, therefore, formulated as a constraint satisfaction problem with an optimal solution determined by the minimum of the energy function. This energy minimum represents a “best guess” map of the spatial distribution of class components in each pixel. The technique was applied to both synthetic and simulated Landsat TM imagery, and the resultant maps provided an accurate and improved representation of the land covers studied, with root mean square errors (RMSEs) for Landsat imagery of the order of 0.09 pixels in the new fine resolution image recorded     

Incremental learning approach based on vector neural network for emitter identification

To deal with the problem of emitter identification (EID) caused by the measurement uncertainty of emitter feature parameters and to realise the automatic updating of the emitter database, which is usually used as emitter templates in identification processing, a vector neural network based incremental learning (VNNIL) approach for EID is proposed. This method combines the vector neural networks (VNNs) and the ensemble-based incremental learning (Learn++) algorithm. The VNN is adopted to construct a weak classifier and the Learn++ is used to generate ensembles of the weak classifiers. Considering that the VNN can realise the non-linear mapping between the interval-value input data and the interval-value output emitter types, and that the Learn++ can update the emitter database automatically, the VNNIL treats the two mentioned problems above as a single one and realises EID and parameters updating at the same time. A number of simulations are presented to demonstrate the identification and updating capability of the VNNIL algorithm. As shown in the simulation results, the VNNIL algorithm not only possesses a better learning and identification capability, but also achieves a better noise adaptability.     

FPGA-implementation of dynamic time delay neural network for power amplifier behavioral modeling

In this paper, we propose new architectures for FPGA-implementation of a dynamic neural network power amplifier behavioral modeling. The real-valued time-delay neural network (RVTDNN) and the backpropagation (BP) learning algorithm were implemented on FPGA using Xilinx system generator for DSP and the Virtex-6 FPGA ML605 evaluation kit. Different RVTDNN architectures are proposed for various values of the number of hidden neurons, the activation function resolution, and the fixed-point data format. These architectures are evaluated and compared in terms of modeling performances and resource utilization using 16-QAM modulated test signal.     

自适应线性神经网络LMM算法的谐波辨识技术研究

介绍了组合适应线性神经网络最小平均值评估法(Adaline-LMM)对脉冲控制信号的拟合分析方法,用于对电力控制系统中的信号评估。通过对系统信号中的各个谐波分量的幅值和相位进行谐波辨识,并对Adaline的权重向量进行更新,同时对目标函数进行技术估计。其中,自适应神经网络中的权重向量由LMM算法进行迭代更新,通过最小平均值估计算法的引入,减小由于脉冲噪声引起的暂时波动的影响。通过对给定脉冲信号进行拟合,可以发现所提方法具有较高的计算精度。     

BP神经网络在红外热波无损检测定量识别中的应用

为实现红外热波检测对缺陷的定量识别,应用BP神经网络,拟合函数关系来实现定量识别。在脉冲热像中,以最佳检测时间和表面最大温差为输入量,以缺陷的深度和直径为输出量,利用BP神经网络拟合输入量与输出量之间的关系。借助数值计算的方法,提供样本训练神经网络,并进行了30次随机计算。通过结果分析,发现使用BP神经网络进行计算具备以下特点:网络收敛速度并不决定计算的精度;网络训练过程中,是否达到计算目标误差不会对计算精度带来较大影响;该方法具有较好的计算稳定性。针对计算结果分布特点,设计计算方法,对数据中的较大误差点进行剔除,最后使用取均值的方法减小获得较大误差的风险,提高计算精度。计算结果表明,在4个参数的计算中,最大误差为3.32%,最小误差为0.1%,证明BP神经网络方法可以用于实现缺陷的定量识别计算。