Expert Diagnosing system for a Rotation Mechanism Based on a Neural Network

By combining the artificial neural network with the rule reasoning expert system,an expert diagnosing system for a rotation mechanism was established.This expert system takes advantage of both a neural network and a rule reasoning expert system;it can also make use of all kinds of knowledge in the repository to diagnose the fault with the positive and negative mixing reasoning mode.The binary system was adopted to denote all kinds of fault in a rotation mechanism.The neural networks were trained with a random parallel algorithm (Alopex).The expert system overcomes the self-learning difficulty of the rule reasoning expert system and the shortcoming of poor system control of the neural network.The expert system developed in this paper has power ful diagnosing ability.     

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.     

Robustness analysis of radial base function and multi-layered feed-forward neural network models

In this paper, two popular types of neural network models (radial base function (RBF) and multi-layered feed-forward (MLF) networks) trained by the generalized delta rule, are tested on their robustness to random errors in input space. A method is proposed to estimate the sensitivity of network outputs to the amplitude of random errors in the input space, sampled from known normal distributions. An additional parameter can be extracted to give a general indication about the bias on the network predictions. The modelling performances of MLF and RBF neural networks have been tested on a variety of simulated function approximation problems. Since the results of the proposed validation method strongly depend on the configuration of the networks and the data used, little can be said about robustness as an intrinsic quality of the neural network model. However, given a data set where ‘pure’ errors from input and output space are specified, the method can be applied to select a neural network model which optimally approximates the nonlinear relations between objects in input and output space. The proposed method has been applied to a nonlinear modelling problem from industrial chemical practice. Since MLF and RBF networks are based on different concepts from biological neural processes, a brief theoretical introduction is given.     

Fast and accurate modelling of frequencyselective surfaces using a new modular neural network configuration of multilayer perceptrons

The authors consider the analysis and modelling of the scattering from frequency-selective surfaces (FSSs), in the 6-14-GHz band, as a function of its periodic array geometry of thin dipole elements on an anisotropic layer. The accurate full-wave electromagnetic (EM) analysis of each FSS was carried out using the method of moments. From the available EM data, the artificial neural network (ANN) models can be developed. The modelling problem was solved by using a new modular configuration of multilayer perceptrons (MLPs), which is an implementation of the proposal modified from the previous knowledge method of neuromodelling information. Each MLP in the modular configuration was trained separately from the others through the resilient backpropagation algorithm. Within the region of interest studied, the ANN model developed is able to estimate the resonance frequencies and the bandwidths of the FSS band-stop filters, with high accuracy and low computational cost. To verify the advantageous properties of the modular MLP/MLP model, a neural model using a simple MLP was developed in order to analyse the same learning task. A comparative study was done between these models in terms of training the convergence, the accuracy and the computational cost.     

Modeling of Magnetic Properties of Nanocrystalline La-doped Barium Hexaferrite

In this paper an artificial neural network (ANN) has been developed to compute the magnetization of the pure and La-doped barium ferrite powders synthesized in ammonium nitrate melt. The input parameters were: the Fe/Ba ratio, La content, sintering temperature, HCl washing and applied magnetic field. A total of 8284 input data set from currently measured 35 different samples with different Fe/Ba ratios, La contents and washed or not washed in HCl were available. These data were used in the training set for the multilayer perceptron (MLP) neural network trained by Levenberg–Marquardt learning algorithm. The hyperbolic tangent and sigmoid transfer functions were used in the hidden layer and output layer, respectively. The correlation coefficients for the magnetization were found to be 0.9999 after the network was trained.     

Neuro-fuzzy control of complex manufacturing processes

A scheme for intelligent optimization and control of complex manufacturing processes is presented. The underlying nonlinear process is modelled by artificial neural networks and process control is performed by fuzzy logic. Fuzzy rules are automatically generated from the trained neural networks through a novel rule generation mechanism and fuzzy control is performed by Mamdani implication. Simulation results show that the proposed approach can provide a robust and accurate way of controlling complex processes without comprehensive models or knowledge about the process.     

Aging process optimization for a copper alloy considering hardness and electrical conductivity

A multi-objective optimization methodology for the aging process parameters is proposed which simultaneously considers the mechanical performance and the electrical conductivity. An optimal model of the aging processes for Cu–Cr–Zr–Mg is constructed using artificial neural networks and genetic algorithms. A supervised artificial neural network (ANN) to model the non-linear relationship between parameters of aging treatment and hardness and conductivity properties is considered for a Cu–Cr–Zr–Mg lead frame alloy. Based on the successfully trained ANN model, a genetic algorithm is adopted as the optimization scheme to optimize the input parameters. The result indicates that an artificial neural network combined with a genetic algorithm is effective for the multi-objective optimization of the aging process parameters.     

An efficient gait recognition based on a selective neural network ensemble

The neural network ensemble is a learning paradigm where a collection of neural networks is trained for the same task. Generally, the ensemble shows better generalization performance than a single neural network. In this article, a selective neural network ensemble is applied to gait recognition. The proposed method selects some neural network based on the minimization of generalization error. Since the selection rule is directly incorporated into the cost function, we can obtain adequate component networks to constitute an ensemble. Experiments are performed with the NLPR database to show the performance of the proposed algorithm. © 2008 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 18, 237–241, 2008; Published online in Wiley InterScience (www.interscience.wiley.com).     

A neural network method for restoring the initial impurity concentration distribution from data of ion sputter depth profiling

A new approach to solving the problem of restoring the initial impurity concentration distribution from data of ion sputter depth profiling is proposed. The algorithm of impurity profile restoration is based on using an artificial neural network with the input signals representing surface concentrations of impurity determined at sequential moments of sputter depth profiling. The artificial neural network is trained for various depths and thicknesses of the impurity-containing layer and various values of parameters of the adopted model equation of diffusion-like ion mixing.     

模糊中心聚类的模式识别学习方法

基于一个约束条件下的非线性规划问题的优化计算思想，把模糊中心聚类中计算输入矢量与中心的距离来实现聚类作为一种优化计算问题，证明了模糊中心聚类方法，取一个适当的属函数，其聚类中心vi为模糊聚类中心价值函数的极小值，推导出了基于模糊中心聚类的模式识别的无导师递推学习方法，提出了模糊中心聚类模式分类神经网络结构，该网络可以实现并行数据处理和模式分类的软划分和硬划分。     

Wavelet neural network (WNN) approach for calibration model building based on gasoline near infrared (NIR) spectra

In this paper we have compared the abilities of two types of artificial neural networks (ANN): multilayer perceptron (MLP) and wavelet neural network (WNN) — for prediction of three gasoline properties (density, benzene content and ethanol content). Three sets of near infrared (NIR) spectra (285, 285 and 375 gasoline spectra) were used for calibration models building. Cross-validation errors and structures of optimized MLP and WNN were compared for each sample set. Four different transfer functions (Morlet wavelet and Gaussian derivative – for WNN; logistic and hyperbolic tangent – for MLP) were also compared. Wavelet neural network was found to be more effective and robust than multilayer perceptron.     

Interpreting Knowledge Representations in BP-SOM

Artificial Neural Networks (anns) are able, in general and in principle, to learn complex tasks. Interpretation of models induced by anns, however, is often extremely difficult due to the non linear and non-symbolic nature of the models. To enable better interpretation of the way knowledge is represented in anns, we present bp-som, a neural network architecture and learning algorithm. bp-som is a combination of a multi -layered feed-forward network (mfn) trained with the back-propagation learning rule (bp), and Kohonen’s self-organising maps (soms). The involvement of the som in learning leads to highly structured knowledge representations both at the hidden layer and on the soms. We focus on a particular phenomenon within trained bp-som networks, viz. that the som part acts as an organiser of the learning material into instance subsets that tend to be homogeneous with respect to both class labelling and subsets of attribute values. We show that the structured knowledge representation can either be exploited directly for rule extraction, or be used to explain a generic type of checksum solution found by the network for learning M-of-N tasks.

     

Chemometric classification of several olive cultivars from Trás-os-Montes region (northeast of Portugal) using artificial neural networks

This work aimed to use artificial neural networks for fruit classification according to olive cultivar, as a tool to guarantee varietal authenticity. So, 70 samples, each one containing, in general, 40 olives, belonging to the six most representative olive cultivars of Trás-os-Montes region (Cobrançosa, Cordovil, Madural, Negrinha de Freixo, Santulhana and Verdeal Transmontana) were collected in different groves and during four crop years. Five quantitative morphological parameters were evaluated for each fruit and endocarp, respectively. In total, ten biometrical parameters were used together with a multilayer perceptron artificial neural network allowing the implementation of a classification model. Its performance was compared with that obtained using linear discriminant analysis. The best results were obtained using artificial neural networks. In fact, the external validation procedure for linear discriminant analysis, using olive data from olive trees not included in the model development, showed an overall sensibility and specificity in the order of 70% and varying between 45 and 97% for the individual cultivars. On the other hand, the artificial neural network model was able to correctly classify the same unknown olives with a global sensibility and specificity around 75%, varying from 58 and 95% for each cultivar. The predictive results of the artificial neural network model selected was further confirmed since, in general, it correctly or incorrectly classified the unknown olive fruits in each one of the six cultivars studied with, respectively, higher and lower probabilities than those that could be expected by chance. The satisfactory results achieved, even when compared with previous published works, regarding olive cultivar's classification, show that the neural networks could be used by olive oil producers as a preventive and effective tool for avoiding adulterations of Protected Designation of Origin or monovarietal olive oils with olives of non-allowed cultivars.     

Impact Location and Quantification on a Composite Panel using Neural Networks and a Genetic Algorithm

Abstract: The problem of impact detection in composite panels using artificial neural networks is addressed in this paper. The data were taken from an experiment in which time dependent strain data were recorded on a network of surface-mounted piezoceramic sensors when the plate was impacted. Neural networks were trained to locate and quantify the impact event when presented with features extracted from the measured data. An important problem for detection systems like this is that of optimal sensor placement; this is solved here by means of a Genetic Algorithm. The study shows that a relatively small number of sensors can be used to detect reliably impacts on a composite plate.     

基于神经网络的遥感图像分类的新方法研究

随着神经网络理论的深入研究,人工神经网络在遥感图像分析与处理的各个方面都有广泛的适用性,并且已经取得了较好的效果,是遥感信息提取的一种有效途径。本文介绍了BP神经网络在遥感影像分类中的应用,通过自适应和在网络权值调整过程中加入特征因子算法,并结合Matlab软件,改进了BP神经网络的优化算法,使网络对误差变化敏感且收敛速度陕,减少了人为因素的干预,改善了学习速率和网络的适应能力,而且精度可靠。     

一种基于软计算的转子故障诊断方法

提出了一种基于软计算的转子故障诊断方法。该方法充分利用软计算中的模糊集合理论,人工神经网 络,粗糙集理论和遗传算法等计算方法优势,弥补它们相互的不足,进行故障诊断。首先利用粗糙集理论对样本数据进 行初步规则获取,并计算规则的依赖度和条件覆盖度,然后根据这些规则进行网络设计,其中,网络隐层节点的数目等于 规则的数目,初始网络权重由规则的依赖度和条件覆盖度确定,最后用遗传算法对模糊神经网络参数进行优化。使用该 网络对转子类常见故障进行诊断。实验表明,和一般模糊神经网络相比,这种基于软计算的诊断方法具有训练时间短、 诊断准确率高的特点。     

Neural network that incorporates direct optical imaging

A compact neural network architecture is described that can be trained to sense and classify an optical image directly projected onto it. The system is based on the combination of a two-dimensional amorphous silicon photoconductor array and a liquid-crystal spatial light modulator. Appropriate filtering of the incident optical image on capture is incorporated into the network training rules through a modification of the standard backpropagation training algorithm. Training of the network on two image-classification problems is described: the recognition of handprinted digits and facial recognition. The network, once trained, is capable of stand-alone operation, sensing an incident image, and outputting a final classification signal in real time.     

A hybrid computer simulation-artificial neural network algorithm for optimisation of dispatching rule selection in stochastic job shop scheduling problems

Industrial systems are constantly subject to random events with inevitable uncertainties in production factors, especially in processing times. Due to this stochastic nature, selecting appropriate dispatching rules has become a major issue in practical problems. However, previous research implies that using one dispatching rule does not necessarily yield an optimal schedule. Therefore, a new algorithm is proposed based on computer simulation and artificial neural networks (ANNs) to select the optimal dispatching rule for each machine from a set of rules in order to minimise the makespan in stochastic job shop scheduling problems (SJSSPs). The algorithm contributes to the previous work on job shop scheduling in three significant ways: (1) to the best of our knowledge it is the first time that an approach based on computer simulation and ANNs is proposed to select dispatching rules; (2) non-identical dispatching rules are considered for machines under stochastic environment; and (3) the algorithm is capable of finding the optimal solution of SJSSPs since it evaluates all possible solutions. The performance of the proposed algorithm is compared with computer simulation methods by replicating comprehensive simulation experiments. Extensive computational results for job shops with five and six machines indicate the superiority of the new algorithm compared to previous studies in the literature.     

Neural-network classifiers for automatic real-world aerial image recognition

We describe the application of the multilayer perceptron (MLP) network and a version of the adaptive resonance theory version 2-A (ART 2-A) network to the problem of automatic aerial image recognition (AAIR). The classification of aerial images, independent of their positions and orientations, is required for automatic tracking and target recognition. Invariance is achieved by the use of different invariant feature spaces in combination with supervised and unsupervised neural networks. The performance of neural-network-based classifiers in conjunction with several types of invariant AAIR global features, such as the Fourier-transform space, Zernike moments, central moments, and polar transforms, are examined. The advantages of this approach are discussed. The performance of the MLP network is compared with that of a classical correlator. The MLP neural-network correlator outperformed the binary phase-only filter (BPOF) correlator. It was found that the ART 2-A distinguished itself with its speed and its low number of required training vectors. However, only the MLP classifier was able to deal with a combination of shift and rotation geometric distortions.     

Characterization of Al/SiC Nanocomposite Prepared by Mechanical Alloying Process Using Artificial Neural Network Model

An artificial neural network model was developed for modeling of the effects of mechanical alloying process parameters including milling time, milling speed, and ball-to-powder weight ratio on the crystallite size and lattice strain of the aluminum for Al/SiC nanocomposite powders. A Multilayer Perceptron (MLP) and Radial Basis Function (RBF) networks were used. It was found that MLP network yields better results compared to RBF network with a high correlation coefficients. The neural network model in agreement with other experimental results and theories was shown the variations of the crystallite size and lattice strain of the aluminum against the process parameters.