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.     

基于自适应递阶遗传算法的神经网络优化策略

基于递阶结构的遗传算法可以同时对多层前向神经网络进行结构优化和权重求解。与基本的遗传算法相比，这种算法不仅在权重训练方面更加快速稳定，而且能在学习过程中确定网络的拓扑结构，具有较高的学习效率，而在遗传过程中采用自适应的交叉和变异概率能有效加快遗传速度和避免早熟现象的出现。     

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.     

基于广义回归神经网络的传感器非线性误差校正

介绍了径向基函数网络的函数逼近原理和方法,提出了一种基于广义回归神经网络(GRNN)的传感器非线性误差校正方法。通过Matlab的Network Toolbox(神经网络工具箱),GRNN训练程序实现了输出特性曲线逼近。仿真分析表明:GRNN能够很好地满足传感器非线性拟合的要求,网络结构简单,收敛速度快。     

基于遗传算法和BP网络的物体识别方法

目前常用的物体识别方法,其过程非常复杂,信息量和计算量都很大.结合遗传算法的神经网络方法,充分利用GA的全局搜索能力、BP算法的局部搜索能力和鲁棒性强的特性,提出了一种用遗传算法全局优化神经网络拓扑结构和网络权值的新编码方案进行物体识别方法.仿真结果表明,该方法既解决了BP神经网络对初始权值敏感和容易局部收敛的问题,又加快GA.BP网络的收敛速度,提高收敛精度且识别率较高,从而验证了该方法的有效性.     

A comparison of neural network and multiple regression analysis in modeling capital structure

Empirical studies of the variation in debt ratios across firms have used statistical models singularly to analyze the important determinants of capital structure. Researchers, however, rarely employ non-linear models to examine the determinants and make little effort to identify a superior prediction model. This study adopts multiple linear regressions and artificial neural networks (ANN) models with seven explanatory variables of corporation’s feature and three external macro-economic control variables to analyze the important determinants of capital structures of the high-tech and traditional industries in Taiwan, respectively. Results of this study show that the determinants of capital structure are different in both industries. The major different determinants are business-risk and growth opportunities. Based on the values of RMSE, the ANN models achieve a better fit and forecast than the regression models for debt ratio, and ANNs are cable of catching sophisticated non-linear integrating effects in both industries. It seems that the relationships between debt ratio and independent variables are not linear. Managers can apply these results for their dynamic adjustment of capital structure in achieving optimality and maximizing firm’s value.     

Modeling and optimization for microstructural properties of Al/SiC nanocomposite by artificial neural network and genetic algorithm

Mechanical alloying process for synthesizing of Al/SiC nanocomposite powders was modeled by artificial neural network and then optimized by genetic algorithm. The feed-forward back propagation neural network model was used for predicting of the characteristics of the nanocomposite. These characteristics were the crystallite size, and the lattice strain of Al matrix. The aim of the optimization was to specify the maximum lattice strain and the minimum crystallite size of aluminum matrix that could be acquired by adjusting the process variables. Process variables included milling time, milling speed, balls to powders weight ratio that they were given as the input of the neural network model. Both modeling and optimization achieved satisfactory performance, and the genetic algorithm system proved to be a powerful tool that can suitably optimize process parameters. A comparison was made with an already carried out work; the model showed 37.6% improvement in error percentage of the crystallite size and 18.7% improvement in error percentage of the lattice strain of aluminum matrix.     

改进的遗传算法在神经网络结构优化中的应用

为了解决人工神经网络隐层节点数目难以确定的问题,针对三层BP神经网络提出了一种最大上限隐层节点数模型,并用改进的遗传算法对其优化。最后,将优化的神经网络对语音特征信号进行分类。仿真结果表明优化后的神经网络具有很好的泛化能力,验证了该方法的有效性。     

Weld appearance prediction with BP neural network improved by genetic algorithm during disk laser welding

The appearance of welds is the external manifestation of welding quality. The morphology of molten pools is significantly associated with the weld appearance, but the approach to measure the morphology of molten pools during laser welding remains an outstanding challenge up to now. In this study, the shadows of molten pools were formed to describe the morphology of molten pools. Principal components analysis (PCA) is applied to analyze the characteristics of the molten pools’ shadow in order to reduce their redundancy. Then BP neural network improved by genetic algorithm (GABP) is established to model the relation between welding appearance and the characteristics of the molten-pool-shadows. The effectiveness of the established model is analyzed through two different welding speed experiments, and the results verify its prediction performance. The work provides an effective way to predict the weld appearance and assess the welding quality in real-time.     

基于遗传神经网络优化的过程信号盲分离算法

研究一种较新的盲信号神经网络分离(BSS)方法,用于过程信号去噪.由于盲信号分离神经网络存在容易陷入局部极小点、收敛速度慢的缺点,研究采用遗传算法优化盲信号分离神经网络权值的初值,将遗传算法与神经网络(HJNN)结合形成GA-HJNN算法,可迅速得到最佳盲信号分离神经网络的权值矩阵,实现对过程信号的去噪,并通过实验对2种算法进行了比较.     

遗传算法优化BP网络及其在灰渣粘度预测中的应用

气流床气化技术受到广泛关注,一般工艺要求液态排渣,而灰渣粘度决定着气化炉排渣能否顺利。在灰渣粘度预测中,粘度与灰渣呈复杂的非线性映射关系,而目前尚未有成熟的模型。本文拟从模糊模型人手,采用遗传算法(GA)优化神经网络(BP)的初始权阈值,优化后的神经网络模型,再预测灰渣粘度值。预测过14组样本,将预测值同三种不同机理模型预测值比较,证明GA-BP模型预测值同实验值最接近,且精度明显较其它模型高。     

Identification of time-series signals using a dynamic neural network with GA-based training

We propose a dynamic neural network (DNN) that realizes a dynamic property and has a network structure with the properties of inertia, viscosity, and stiffness without time-delayed input elements, and a training algorithm based on a genetic algorithm (GA). In a previous study, we proposed a modified training algorithm for the DNN based on the error back-propagation method. However, in the previous method it was necessary to determine the values of the DNN property parameters by trial and error. In the newly proposed DNN, the GA is designed to train not only the connecting weights but also the property parameters of the DNN. Simulation results show that the DNN trained by the GA obtains good performance for time-series patterns generated from an unknown system, and provides a higher performance than the conventional neural network. This work was presented in part at the 10th International Symposium on Artificial Life and Robotics, 0ita, Japan, February 4–6, 2005     

自适应遗传算法优化神经网络的入侵检测研究

入侵检测是一种动态的安全防护技术,能够对网络内部、外部攻击进行防御.基于神经网络的入侵检测是常见的智能入侵检测方法.针对神经网络算法易陷入局部极值和简单遗传算法收敛速度慢的问题,提出了一种将神经网络和遗传算法相结合,用遗传算法优化神经网络权值,在遗传算法优化神经网络时采用自适应遗传操作.将自适应遗传算法优化神经网络算法应用于入侵检测系统中,实验结果表明,该方法能够有效的提高系统的检测率,降低误报率和漏报率.     

基于遗传神经网络的相似重复记录检测方法

为了有效解决数据清洗领域中相似重复记录的检测问题,提出了一种基于遗传神经网络的相似重复记录检测方法.该方法计算两条记录对应字段间的相似度,构建基于神经网络的检测模型,利用遗传算法对网络模型的权值进行优化,使用遗传神经网络组合多个字段上的相似度来检测相似重复记录.在不同领域数据集上的测试结果表明,该方法能够提高相似重复记录检测的准确率和检测精度.     

多层前向神经网络的快速学习算法及其应用

针对目前多层前向神经网络学习算法存在的不足,提出一种多层前向神经网络的快速学习算法,它不仅符合生物神经网络的基本特征,而且算法简单,学习收敛速度快,具有线性、非线性逼近精度高等特性.以二杆机械手逆运动学建模作为应用实例,仿真结果表明该方法是有效的,其算法与收敛速度更优于BP网络.     

支持向量机与RBF神经网络回归性能比较研究

支持向量机与RBF神经网络相比各有优缺点,通过对支持向量机与RBF神经网络的研究,从理论上分析了这两种学习机在回归预测原理上的异同,通过仿真实验对比了两者在测试集上的逼近能力及泛化能力。仿真结果表明,对于小样本集,支持向量机的逼近能力及泛化能力要优于RBF神经网络。对实际应用中回归模型的选择问题提出了建议。     

Artificial neural networks and genetic algorithm for bearing fault detection

A study is presented to compare the performance of three types of artificial neural network (ANN), namely, multi layer perceptron (MLP), radial basis function (RBF) network and probabilistic neural network (PNN), for bearing fault detection. Features are extracted from time domain vibration signals, without and with preprocessing, of a rotating machine with normal and defective bearings. The extracted features are used as inputs to all three ANN classifiers: MLP, RBF and PNN for two- class (normal or fault) recognition. Genetic algorithms (GAs) have been used to select the characteristic parameters of the classifiers and the input features. For each trial, the ANNs are trained with a subset of the experimental data for known machine conditions. The ANNs 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 preprocessing techniques are investigated. The results show the effectiveness of the features and the classifiers in detection of machine condition.     

Visual quality recognition of nonwovens using generalized Gaussian density model and robust Bayesian neural network

This work is dedicated to develop an algorithm for the visual quality recognition of nonwoven materials, in which image analysis and neural network are involved in feature extraction and pattern recognition stage, respectively. During the feature extraction stage, each image is decomposed into four levels using the 9-7 bi-orthogonal wavelet base. Then the wavelet coefficients in each subband are independently modeled by the generalized Gaussian density (GGD) model to calculate the scale and shape parameters with maximum likelihood (ML) estimator as texture features. While for the recognition stage, the robust Bayesian neural network is employed to classify the 625 nonwoven samples into five visual quality grades, i.e., 125 samples for each grade. Finally, we carry out the outlier detection of the training set using the outlier probability and select the most suitable model structure and parameters from 40 Bayesian neural networks using the Occam's razor. When 18 relevant textural features are extracted for each sample based on the GGD model, the average recognition accuracy of the test set arranges from 88% to 98.4% according to the different number of the hidden neurons in the Bayesian neural network.     

Disaggregation & aggregation of time series components: A hybrid forecasting approach using generalized regression neural networks and the theta method

A hybrid forecasting method is proposed which leverages from statistical and neural network techniques to perform multi-step ahead forecasting. The proposed method is based on the disaggregation of time series components, the prediction of each component individually and the reassembling of the extrapolations to obtain an estimation for the global data. The STL decomposition procedure from the literature [5] is implemented to obtain the seasonal, trend and irregular components of the time series, whilst Generalized Regression Neural Networks (GRNN) [12] are used to perform out-of sample extrapolations of the seasonal and residual components. The univariate Theta model is employed for the estimation of the directional component. The application of the GRNN is based on the dynamic calibration of the training process for each of the seasonal and irregular components individually. The proposed hybrid forecasting method is applied to 60 time series from the NN3 competition and 227 time series from the M1 Competition dataset, to obtain 18 out-of sample predictions. The results from the application demonstrate that the proposed method can outperform standard statistical techniques in the literature. One of the main contributions of the current research lies in the investigation of the strengths and weaknesses of the GRNN in extrapolating structural components of time series.     

A hybrid multi-objective approach based on the genetic algorithm and neural network to design an incremental cellular manufacturing system

One important issue related to the implementation of cellular manufacturing systems (CMSs) is to decide whether to convert an existing job shop into a CMS comprehensively in a single run, or in stages incrementally by forming cells one after the other, taking the advantage of the experiences of implementation. This paper presents a new multi-objective nonlinear programming model in a dynamic environment. Furthermore, a novel hybrid multi-objective approach based on the genetic algorithm and artificial neural network is proposed to solve the presented model. From the computational analyses, the proposed algorithm is found much more efficient than the fast non-dominated sorting genetic algorithm (NSGA-II) in generating Pareto optimal fronts.