Antinoise approximation of the lidar signal with wavelet neural networks

We propose a new, to our knowledge, denoising method for lidar signals based on a regression model and a wavelet neural network (WNN) that permits the regression model not only to have a good wavelet approximation property but also to make a neural network that has a self-learning and adaptive capability for increasing the quality of lidar signals. Specifically, we investigate the performance of the WNN for antinoise approximation of lidar signals by simultaneously addressing simulated and real lidar signals. To clarify the antinoise approximation capability of the WNN for lidar signals, we calculate the atmosphere temperature profile with the real signal processed by the WNN. To show the contrast, we also demonstrate the results of the Monte Carlo moving average method and the finite impulse response filter. Finally, the experimental results show that our proposed approach is significantly superior to the traditional methods.     

基于神经网络和功率谱的水中目标信号检测方法

将功率谱和神经网络相结合，应用于高海况、低信噪比条件下，水中目标信号的特征提取中．文中首先对信号进行功率谱估计，利用目标信号功率主要集中在低频部分的特点，提取低频信号的能量作为特征，然后利用人工神经网络对目标信号进行检测．利用不同浪级情况下海洋水压场的仿真信号数据，对某型目标舰船的水压信号进行了检测计算，验证了该方法的有效性，尤其是达到了在高海况、低信噪比条件下，对目标信号检测率比较高、虚警率比较低的效果．     

Hybrid discrete-time neural networks

Hybrid dynamical systems combine evolution equations with state transitions. When the evolution equations are discrete-time (also called map-based), the result is a hybrid discrete-time system. A class of biological neural network models that has recently received some attention falls within this category: map-based neuron models connected by means of fast threshold modulation (FTM). FTM is a connection scheme that aims to mimic the switching dynamics of a neuron subject to synaptic inputs. The dynamic equations of the neuron adopt different forms according to the state (either firing or not firing) and type (excitatory or inhibitory) of their presynaptic neighbours. Therefore, the mathematical model of one such network is a combination of discrete-time evolution equations with transitions between states, constituting a hybrid discrete-time (map-based) neural network. In this paper, we review previous work within the context of these models, exemplifying useful techniques to analyse them. Typical map-based neuron models are low-dimensional and amenable to phase-plane analysis. In bursting models, fast-slow decomposition can be used to reduce dimensionality further, so that the dynamics of a pair of connected neurons can be easily understood. We also discuss a model that includes electrical synapses in addition to chemical synapses with FTM. Furthermore, we describe how master stability functions can predict the stability of synchronized states in these networks. The main results are extended to larger map-based neural networks.     

人工神经网络的单调序列逼近

设Nn,Ф是以Ф为激活函数的具有n+1个神经元的前向单隐层人工神经网络的全体.主要证明了,若f∈C[0,1],则对于任意的ε>0,存在两个神经网络序列{Pn,Ф}和{Qn,Ф},使得在[0,1]上Qn,Ф(x)≤Qn+1,Ф(x)≤f(x)≤Pn+1,Ф(x)≤Pn,Ф(x),而且Pn,Ф(x)-Qn,Ф(x)≤(6+4(2~(1/2)))En,Ф(f),这里的En,Ф(f)为Nn,Ф中的元对f的最佳逼近.     

Improvement in transient piezoelectric responses of NDE transceivers using selective damping and tuning networks

NDE ultrasonic applications for quality control purposes, based on piezoelectric devices working as pulsed ultrasonic probes, usually include some tuning circuit either across the pulser output connectors or close to the piezoelectric probe electrodes. Classic criteria to determine tuning parameters in ultrasonic transducers do not perform very well in transient regime under the typical electrical conditions and peculiar output impedances of the required high-voltage pulsers. In most practical situations, the tuning values are manually adjusted in specific circuits for each NDE case, testing each channel of a multiprobe inspection system to find the best sensitivity. In this paper, the positive influences of certain selective damping and tuning networks on the time and frequency behaviors of NDE piezoelectric transceivers are analyzed in detail. Different waveforms and spectra of received ultrasonic signals are comparatively evaluated for different real tuning conditions, after passing through two ultrasonic processes linked in cascade: a transmitter of industrial type and a broad-band ultrasonic receiver. Piezoelectric transducer impedances, transduction transfer functions, and HV output spikes from a piezoelectrically loaded NDE pulser, are computed, to identify separately the influence of each stage. In order to improve amplitude and axial resolution in the received transient responses, relative contributions from emission and reception tuning networks are individually evaluated for a particular NDE two-stage piezoelectric channel. Also shown are the final experimental results relative to the ultrasonic test pulse and detected in a through-transmission NDE configuration, gradually improved by using distinct tuning levels.     

Job-shop scheduling using neural networks

Complete enumeration of all sequences to establish global optimality is not feasible as the search space; for a general job-shop scheduling problem,PiG has an upper bound of (n!)m. Since the early fifties a great deal of research attention has been focused on solving PiG , resulting in a wide variety of approaches such as branch and bound, simulated annealing, tabu search, etc. However, limited success has been achieved by these methods due to the shear intractability of this generic scheduling problem. Recently, much effort has been concentrated on using neural networks to solve PiG as they are capable of adapting to new environments with little human intervention and can mimic thought processes. Major contributions in solving PiG using a Hopfield neural network, as well as applications of back-error propagation to general scheduling problems are presented. To overcome the deficiencies in these applications a modified back-error propagation model, a simple yet powerful architecture which can be successfully simulated on a personal computer, is applied to solve PiG.     

一类神经网络逼近可积函数

用连续模刻画了实轴上Cardaliguet-Eurrard型神经网络算子逼近连续函数速度的上界估计,同时,对于Lebesgue可积函数的逼近,构造相应的神经网络算子,并且给出其逼近速度的Jackson型估计.     

Using neural networks to study networks of scientific journals

In this paper a new approach to study science dynamics is introduced. This approach is based in the use of Kohonen preserving topology maps, a kind of neural network. Four data set consisting in cross-citation matrix are studied using this approach. Relations maps and domains maps are computed for these data sets and interrelationships among journals are studied. This approach allow to stude both, hierarchical journal structure in a given time and evolution of relations among journals in a given time lag.     

Eddy-current signal analysis and inversion for semielliptical surface cracks

A scalar potential formulation of the Z formula for the change in impedance of an eddy-current probe caused by a surface-breaking flaw is developed. The resulting formula is evaluated using a finite-difference method, which permits calculation of Z for semielliptical flaws. The numerical results are checked by comparing calculations for rectangular-shaped flaws to previous calculations using an analytical solution for this geometry. Theoretical results are then verified by comparison with measurements on semielliptical fatigue cracks and EDM notches in aluminum alloy specimens using air-core eddy-current probes. An inversion method that compares features of the flaw profile, obtained by scanning the eddy-current probe along the length of the flaw, to a theoretical inversion chart (McFetridge chart) is demonstrated using the experimental data.     

火灾探测的人工神经网络方法

火灾早期探测是较复杂和有重大义意的问题，本文指出了传统火灾探测方法所存在的问题，在分析火灾实验数据的基础上，提出了一种新的火灾探测方法，即人工神经网络（ＡＮＮ）方法。由于ＡＮＮ方法具有自学习能力，能适应各种复杂条件，因此它能克服传统火灾探测方法的缺陷，摸拟实验结果也表明ＡＮＮ方法在火灾自动探测中是一种十分有效的方法     

Characterization of random rough surfaces from scattered intensities by neural networks

Abstract

Optical scatterometry, a non-invasive characterization method, is used to infer the statistical properties of random rough surfaces. The Gaussian model with rms-roughness [sgrave] and correlation length σ is considered in this paper but the employed technique is applicable to any representation of random rough surfaces. Surfaces with wide ranges of Λ and σ, up to 5 wavelengths (λ), are characterized with neural networks. Two models are used: self-organizing map (SOM) for rough classification and multi-layer perceptron (MLP) for quantitative estimation with nonlinear regression. Models infer Λ and σ from scattering, thus involving the inverse problem. The intensities are calculated with the exact electromagnetic theory, which enables a wide range of parameters. The most widely known neural network model in practise is SOM, which we use to organize samples into discrete classes with resolution ΔΛ = Δσ = 0.5λ. The more advanced MLP model is trained for optimal behaviour by providing it with known parts of input (scattering) and output (surface parameters). We show that a small amount of data is sufficient for an excellent accuracy on the order of 0.3λ and 0.15λ for estimating Λ and σ, respectively.     

Optical interconnection for neural networks by use of a self-imaging function

A grating-lens combination unit is developed to form a scalingself-transform function that can self-image on scale. Then an arrayof many such grating-lens units is used for the optical interconnectionof a two-dimensional neural network, and experiments are carriedout. We find that our idea is feasible, the optical interconnectionsystem is simple, and optical adjustment is easy.     

Artificial neural networks for pattern recognition

This tutorial article deals with the basics of artificial neural networks (ANN) and their applications in pattern recognition. ANN can be viewed as computing models inspired by the structure and function of the biological neural network. These models are expected to deal with problem solving in a manner different from conventional computing. A distinction is made between pattern and data to emphasize the need for developing pattern processing systems to address pattern recognition tasks. After introducing the basic principles of ANN, some fundamental networks are examined in detail for their ability to solve simple pattern recognition tasks. These fundamental networks together with the principles of ANN will lead to the development of architectures for complex pattern recognition tasks. A few popular architectures are described to illustrate the need to develop an architecture specific to a given pattern recognition problem. Finally several issues that still need to be addressed to solve practical problems using ANN approach are discussed. This paper is mostly a consolidation of work reported by several researchers in the literature, some of which is cited in the references. The author has borrowed several ideas and illustrations from the references quoted in this paper.     

Parameter estimation using compensatory neural networks

Proposed here is a new neuron model, a basis for Compensatory Neural Network Architecture (CNNA), which not only reduces the total number of interconnections among neurons but also reduces the total computing time for training. The suggested model has properties of the basic neuron model as well as the higher neuron model (multiplicative aggregation function). It can adapt to standard neuron and higher order neuron, as well as a combination of the two. This approach is found to estimate the orbit with accuracy significantly better than Kalman Filter (KF) and Feedforward Multilayer Neural Network (FMNN) (also simply referred to as Artificial Neural Network, ANN) with lambda-gamma learning. The typical simulation runs also bring out the superiority of the proposed scheme over Kalman filter from the standpoint of computation time and the amount of data needed for the desired degree of estimated accuracy for the specific problem of orbit determination.     

Process cost modelling using neural networks

One method of establishing cost-estimating relationships (CERs) that appears to offer advantages is that of neural networks as they are 'model-free estimators', hence providing key advantages over traditional techniques, e.g. regression analysis. However, the complexity of neural network (NN) structures and the availability of a wide range of structural alternatives may make it difficult to select an appropriate network for a specific cost-modelling requirement. The current work is aimed at resolving this issue by examining the use of the Taguchi methodology for identifying NN structural elements. The Taguchi methodology has been used to identify 'best' and 'poorest' NN structures and the models developed using these structures compared with regression-based models in terms of their estimating accuracy. The robustness of these models is then examined in terms of the effects on their estimating accuracy of varying the number of data points used in their development and varying the number of predictor variables within the models.     

基于神经网络的电机转矩软测量研究

径向基函数神经网络（RBFNN）具有最优逼近和全局逼近的特性，在函数拟合方面优于传统的BP网络，将在化工领域广泛使用的软测量技术应用于电机系统的转矩测量，该方法的可行性进行了论证，并运用RBF神经网络建立转矩的软测量模型。同时建立了基于BP神经网络的软测量模型，用改进的kvenberg—Marquardt算法对BP神经网络进行学习和训练，并对两种网络进行了对比。该方法只需要电流信息，辨识方法简单。研究表明，RBF神经网络辨识效果优于BP神经网络。     

Optical finite impulse response neural networks

The finite impulse response neural network is described in detail. Different algorithms capable of temporal back-propagation are considered, including a novel modification to the conventional algorithm, called the delayed-feedback back-propagation algorithm. We present and analyze different optoelectronic processors making use of adaptive volume holograms and three-dimensional optical processing. Two single-layer architectures are presented: the input delay plane architecture and the output delay plane architecture. By combining them it is possible to implement both forward and backward propagation in two multi-layer architectures: the first making use of the conventional temporal back-propagation and the second making use of delayed-feedback back-propagation.     

Ultrasonic robot eyes using neural networks

In order to construct a robot eye using ultrasonic waves in air, a system that combines existing acoustical holography with neural networks is devised. In this system, neural networks are used to reduce the vagueness of reconstruction from the acoustical holography. The system is able to identify objects and to reconstruct the acoustical images of objects using an array of only a small number of receivers. Some experimental results on identification and reconstruction using several kinds of metal plate objects are reported.