Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection

A study is presented to compare the performance of bearing fault detection using two different classifiers, namely, artificial neural networks (ANNs) and support vector machines (SMVs). The time-domain vibration signals of a rotating machine with normal and defective bearings are processed for feature extraction. The extracted features from original and preprocessed signals are used as inputs to the classifiers for two-class (normal or fault) recognition. The classifier parameters, e.g., the number of nodes in the hidden layer in case of ANNs and the radial basis function kernel parameter (width) in case of SVMs along with the selection of input features are optimized using genetic algorithms. The classifiers are trained with a subset of the experimental data for known machine conditions and are tested using the remaining set of data. The procedure is illustrated using the experimental vibration data of a rotating machine. The roles of different vibration signals and signal preprocessing techniques are investigated. The results show the effectiveness of the features and the classifiers in detection of machine condition.     

Learning experiments with genetic optimization of a generalized regression neural network

This paper reports a study unifying optimization by genetic algorithm with a generalized regression neural network. Experiments compare hill-climbing optimization with that of a genetic algorithm, both in conjunction with a generalized regression neural network. Controlled data with nine independent variables are used in combination with conjunctive and compensatory decision forms, having zero percent and 10 percent noise levels. Results consistently favor the GRNN unified with the genetic algorithm.     

Wavelet based fault detection in analog VLSI circuits using neural networks

This paper deals with a new method of testing analog VLSI circuits, using wavelet transform for analog circuit response analysis and artificial neural networks (ANN) for fault detection. Pseudo-random patterns generated by Linear Feedback Shift Register (LFSR) are used as input test patterns. The wavelet coefficients obtained for the fault-free and faulty cases of the circuits under test (CUT) are used to train the neural network. Two different architectures, back propagation and probabilistic neural networks are trained with the test data. To minimize the neural network architecture, normalization and principal component analysis are done on the input data before it is applied to the neural network. The proposed method is validated with two IEEE benchmark circuits, namely, the operational amplifier and state variable filter.     

A review of genetic algorithms applied to training radial basis function networks

The problems associated with training feedforward artificial neural networks (ANNs) such as the multilayer perceptron (MLP) network and radial basis function (RBF) network have been well documented. The solutions to these problems have inspired a considerable amount of research, one particular area being the application of evolutionary search algorithms such as the genetic algorithm (GA). To date, the vast majority of GA solutions have been aimed at the MLP network. This paper begins with a brief overview of feedforward ANNs and GAs followed by a review of the current state of research in applying evolutionary techniques to training RBF networks.     

Flood simulation using parallel genetic algorithm integrated wavelet neural networks

The conventional means of flood simulation and prediction using conceptual hydrological model or artificial neural network (ANN) has provided promising results in recent years. However, it is usually difficult to obtain ideal flood reproducing due to the structure of hydrological model. Back propagation (BP) algorithm of ANN may also reach local optimum when training nodal weights. To improve the mapping capability of neural networks, wavelet function was adopted (WANN) to strengthen the non-linear simulation accuracy and generality. In addition, genetic algorithm is integrated with WANN (GAWANN) to avoid reaching local optimum. Meanwhile, Message Passing Interface (MPI) subroutines are introduced for distributed implement considering the time consumption during nodal weights training. The GAWANN was applied in the flood simulation and prediction in arid area. The test results of 4 independent cases were compared to reveal the relations between historical rainfall and runoff under different time lags. The simulation was also carried out with Xinanjiang model to demonstrate the capability of GAWANN. The numerical experiments in this paper indicated that the parallel GAWANN has strong capability of rain-runoff mapping as well as computational efficiency and is suitable for applications of flood simulation in arid areas.     

Human face detection with neural networks and the DIRECT algorithm

Based on Rowley’s approach, this article proposes a new architecture that uses a specific optimization technique, the DIRECT (DIviding RECTangle) algorithm, to improve the efficiency of face detection in images. The system consists of two main parts: a neural network-based face detection arbitrator, and a search strategy based on an integer-handling DIRECT algorithm. By the architecture, the number of arbitrations is dramatically reduced, and human faces, if they are present in an image, are not restricted to predetermined resolutions and aspect ratios. Experimental results show that the proposed architecture is efficient in terms of both speed and robustness. This work was presented in part at the 12th International Symposium on Artificial Life and Robotics, Oita, Japan, January 25–27, 2007     

On the versatility of radial basis function neural networks: A case study in the field of intrusion detection

Classifiers based on radial basis function neural networks have a number of useful properties that can be exploited in many practical applications. Using sample data, it is possible to adjust their parameters (weights), to optimize their structure, and to select appropriate input features (attributes). Moreover, interpretable rules can be extracted from a trained classifier and input samples can be identified that cannot be classified with a sufficient degree of “certainty”. These properties support an analysis of radial basis function classifiers and allow for an adaption to “novel” kinds of input samples in a real-world application. In this article, we outline these properties and show how they can be exploited in the field of intrusion detection (detection of network-based misuse). Intrusion detection plays an increasingly important role in securing computer networks. In this case study, we first compare the classification abilities of radial basis function classifiers, multilayer perceptrons, the neuro-fuzzy system NEFCLASS, decision trees, classifying fuzzy-k-means, support vector machines, Bayesian networks, and nearest neighbor classifiers. Then, we investigate the interpretability and understandability of the best paradigms found in the previous step. We show how structure optimization and feature selection for radial basis function classifiers can be done by means of evolutionary algorithms and compare this approach to decision trees optimized using certain pruning techniques. Finally, we demonstrate that radial basis function classifiers are basically able to detect novel attack types. The many advantageous properties of radial basis function classifiers could certainly be exploited in other application fields in a similar way.     

一种基于改进k-means的RBF神经网络学习方法

针对传统RBF神经网络学习算法构造的网络分类精度不高,传统的k-means算法对初始聚类中心的敏感,聚类结果随不同的初始输入而波动。为了解决以上问题,提出一种基于改进k-means的RBF神经网络学习算法。先用减聚类算法优化k-means算法,消除聚类的敏感性,再用优化后的k-means算法构造RBF神经网络。仿真结果表明了该学习算法的实用性和有效性。     

A new local search based hybrid genetic algorithm for feature selection

This paper presents a new hybrid genetic algorithm (HGA) for feature selection (FS), called as HGAFS. The vital aspect of this algorithm is the selection of salient feature subset within a reduced size. HGAFS incorporates a new local search operation that is devised and embedded in HGA to fine-tune the search in FS process. The local search technique works on basis of the distinct and informative nature of input features that is computed by their correlation information. The aim is to guide the search process so that the newly generated offsprings can be adjusted by the less correlated (distinct) features consisting of general and special characteristics of a given dataset. Thus, the proposed HGAFS receives the reduced redundancy of information among the selected features. On the other hand, HGAFS emphasizes on selecting a subset of salient features with reduced number using a subset size determination scheme. We have tested our HGAFS on 11 real-world classification datasets having dimensions varying from 8 to 7129. The performances of HGAFS have been compared with the results of other existing ten well-known FS algorithms. It is found that, HGAFS produces consistently better performances on selecting the subsets of salient features with resulting better classification accuracies.     

Rotating machine fault detection based on HOS and artificial neural networks

In order to identify the faults of rotating machinery, classification process can be divided into two stages: one is the signal preprocessing and the feature extraction; the other is the recognition process. In the preprocessing and feature extraction stage, the higher-order statistics (HOS) is used to extract features from the vibration signals. In the recognition process, two kinds of neural network classifier are used to evaluate the classification results. These two classifiers are self-organizing feature mapping (SOM) network for collecting data at the initial stage and learning vector quantization (LVQ) network at the identification stage. The experimental results obtained from HOS as preprocessor to extract the features of fault are clearer than those obtained from the power spectrum. In addition, the recognizable rate by using either SOM or LVQ as classifiers is 100%.     

A parallel CORDIC architecture dedicated to compute the Gaussian potential function in neural networks

In this paper, a new parallel hardware architecture dedicated to compute the Gaussian potential function is proposed. This function is commonly utilized in neural radial basis classifiers for pattern recognition as described by Lee [(Neural Networks for Signal Processing. Prentice-Hall, Englewood Cliffs, NJ, 1992)], Girosi and Poggio [(Neural Comput. 1 (1989) 465)], and Musavi et al. (Neural Networks 5 (1992) 595). Attention to a simplified Gaussian potential function which processes uncorrelated features is confined. Operations of most interest included by the Gaussian potential function are the exponential and the square functions. Our hardware computes the exponential function and its exponent at the same time. The contributions of all features to the exponent are computed in parallel. This parallelism reduces computational delay in the output function. The duration does not depend on the number of features processed. Software and hardware case studies are presented to evaluate the new CORDIC.     

Application of radial basis function and feedforward artificial neural networks to the Escherichia coli fermentation process

Radial basis function and feedforward neural networks are considered for modelling of the recombinant Escherichia coli fermentation process. The models use industrial on-line data from the process as input variables in order to estimate the concentrations of biomass and recombinant protein, normally only available from off-line laboratory analysis. The models performances are compared by prediction error and graphical fit using results obtained from a common testing set of fermentation data.     

A forecasting system for car fuel consumption using a radial basis function neural network

A predictive system for car fuel consumption using a radial basis function (RBF) neural network is proposed in this paper. The proposed work consists of three parts: information acquisition, fuel consumption forecasting algorithm and performance evaluation. Although there are many factors affecting the fuel consumption of a car in a practical drive procedure, in the present system the relevant factors for fuel consumption are simply decided as make of car, engine style, weight of car, vehicle type and transmission system type which are used as input information for the neural network training and fuel consumption forecasting procedure. In fuel consumption forecasting, to verify the effect of the proposed RBF neural network predictive system, an artificial neural network with a back-propagation (BP) neural network is compared with an RBF neural network for car fuel consumption prediction. The prediction results demonstrated the proposed system using the neural network is effective and the performance is satisfactory in terms of fuel consumption prediction.     

Evaluation of the performance of backpropagation and radial basis function neural networks in predicting the drill flank wear

This study compares the performance of backpropagation neural network (BPNN) and radial basis function network (RBFN) in predicting the flank wear of high speed steel drill bits for drilling holes on mild steel and copper work pieces. The validation of the methodology is carried out following a series of experiments performed over a wide range of cutting conditions in which the effect of various process parameters, such as drill diameter, feed-rate, spindle speed, etc. on drill wear has been considered. Subsequently, the data, divided suitably into training and testing samples, have been used to effectively train both the backpropagation and radial basis function neural networks, and the individual performance of the two networks is then analyzed. It is observed that the performance of the RBFN fails to match that of the BPNN when the network complexity and the amount of data available are the constraining factors. However, when a simpler training procedure and reduced computational times are required, then RBFN is the preferred choice.     

The automatic model selection and variable kernel width for RBF neural networks

The Orthogonal Least Squares (OLS) algorithm has been extensively used in basis selection for RBF networks, but it is unable to perform model selection automatically because the tolerance ρ must be specified manually. This introduces noise and it is difficult to implement in the parametric complexity of real-time system. Therefore, a generic criterion that detects the optimum number of its basis functions is proposed. In this paper, not only the Bayesian Information Criterion (BIC) method, used for fitness calculation, is incorporated into the basis function selection process of the OLS algorithm for assigning its appropriate number, but also a new method is developed to optimize the widths of the Gaussian functions in order to improve the generalization performance. The augmented algorithm is employed to the Radial Basis Function Neural Networks (RBFNN) for known and unknown noise nonlinear dynamic systems and its performance is compared with the standard OLS; experimental results show that both the efficacy of BIC for fitness calculation and the importance of proper choice of basis function widths are significant.     

Analysis of radar images for rainfall forecasting using neural networks

This paper describes a new approach to the analysis of weather radar data for short-range rainfall forecasting based on a neural network model. This approach consists in extracting synthetic information from radar images using the approximation capabilities of multilayer neural networks. Each image in a sequence is approximated using a modified radial basis function network trained by a competitive mechanism. Prediction of the rain field evolution is performed by analysing and extrapolating the time series of weight values. This method has been compared to the conventional cross-correlation technique and the persistence method for three different rainfall events, showing significant improvement in 30 and 60 min ahead forecast accuracy.     

GPR target detection using a neural network classifier designed by a multi-objective genetic algorithm

Ground Penetrating Radar (GPR) is an electromagnetic sensing technology employed for localization of underground utilities, pipes, and other types of objects. The radargrams typically obtained have a high dimensionality, containing a number of signatures with hyperbolic pattern shapes, and can be processed to retrieve information about the target’s locations, depths and material type of underground soil. The classical Hough Transform approach used to reconstruct these hyperbola shapes is computationally expensive, given the large dimensionality of the radargrams. In literature, several approaches propose to first approximate the location of hyperbolas to small segments through a classification stage, before applying the Hough transform over these segments. However, the published classifiers designed for this task present a relatively complex architecture.Aiming at an improved target localization, we propose an alternative classification methodology. The goal is to classify windows of GPR radargrams into two classes (with or without target) using a neural network radial basis function (RBF), designed via a multi-objective genetic algorithm (MOGA). To capture samples’ fine details, high order statistic cumulant features (HOS) were used. Feature selection was performed by MOGA, with an optional prior reduction using a mutual information (MIFS) approach. The obtained results demonstrate improvement of the classification performance when compared with other models designed with the same data and are among the best results available in the literature, albeit the large reduction in classifier complexity.     

一种基于改进遗传RBF神经网络的传感器动态特性补偿算法

为了改善传感器的动态特性,减小系统测量误差,分析了传感器动态性能补偿的基本原理,提出了一种基于改进型遗传算法(IAGA)和RBF神经网络相结合的补偿算法,给出了用IAGA-RBF补偿算法建立的数学模型,并应用到瓦斯传感器的补偿环节.实验证明,该补偿算法具有响应速度快、计算精度高和工作频带宽的特点,多项动态特性指标都得到了较大的改善,能够有效地用于传感器的动态特性补偿.     

基于改进遗传算法的RBF神经网络结构优化研究

针对RBF神经网络隐含层节点数过多导致网络结构复杂的问题,提出了一种基于改进遗传算法(IGA)的RBF神经网络优化算法。利用IGA优化基于正交最小二乘法的RBF神经网络结构,通过对隐含层输出矩阵的列向量进行全局寻优,从而设计出结构更优的基于IGA的RBF神经网络(IGA-RBF)。将IGA-RBF神经网络的学习算法应用于电子元器件贮存环境温湿度预测模型,与基于正交最小二乘法的RBF神经网络进行比较的结果表明:IGA-RBF神经网络设计出来的网络训练步数减少了44步,隐含层节点数减少了34个,且预测模型得到的温湿度误差较小,拟合精度大于0.95,具有更高的预测精度。     

Optimal partition algorithm of the RBF neural network and its application to financial time series forecasting

A novel neural-network-based method of time series forecasting is presented in this paper. The method combines the optimal partition algorithm (OPA) with the radial basis function (RBF) neural network. OPA for ordered samples is used to perform the clustering for the samples. The centers and widths of the RBF neural network are determined based on the clustering. The difference of the objective functions of the clustering is used to adjust the structure of the neural network dynamically. Thus, the number of the hidden nodes is selected adaptively. The method is applied to stock price prediction. The results of numerical simulations demonstrate the effectiveness of the method. Comparisons with the hard c-means (HCM) algorithm show that the proposed OPA method possesses obvious advantages in the precision of forecasting, generalization, and forecasting trends. Simulations also show that the OPA–orthogonal least squares (OPA–OLS) algorithm, which combines OPA with the OLS algorithm, results in better performance for forecasting trends.