Predicting grinding burn using artificial neural networks

This paper introduces a method for predicting grinding burn using artificial neural networks (ANN). First, the way to model grinding burn via ANN is presented. Then, as an example, the prediction of grinding burn of ultra-strength steel 300M via ANN is given. Very promising results were obtained.     

Study on optimal design of pressure-adjusting spring using artificial neural networks

A preliminary discussion has been carried out on the traditional optimization design method for pressure-adjusting spring of relief valve. Based on the traditional optimization methods about the pressure-adjusting spring of the relief valve and combined with the advantages of neural network, this paper puts forward the optimization method with many parameters and a lot of constraints based on neural network in order to find the maximal inherent frequency. The object function of optimization is transformed into the energy function of the neural network and the mathematical model of neural network optimization about the pressure-adjusting spring of the relief valve is set up in this method which also puts forward its own algorithm. An example of application shows that network convergence gets stable state of minimization object function E, and object function converges to the utmost minimum point with steady function, then best solution is gained, which makes the design plan better. The algorithm of solution for the problem is effective about the optimum design of the pressure-adjusting spring. The specified technical performances of the relief valve are certified by experiments. The results of experiments showed that by configuring pressure-adjusting spring the dynamic performance and working stability of the relief valve are enhanced.     

Feedforward backpropagation artificial neural networks on reconfigurable meshes

The artificial neural networks (ANNs) have been used successfully in applications such as pattern recognition, image processing, automation and control. Majority of today's applications use backpropagate feedforward ANN. In this paper, two methods of P pattern L layer ANN learning on n × n RMESH have been presented. One required memory space of O(nL) but conceptually is simpler to develop and the other uses pipelined approach which reduces the memory requirement to O(L). Both of these algorithms take O(PL) time and are optimal for RMESH architecture.     

A novel solution for maze traversal problems using artificial neural networks

In this paper we have addressed the problem of finding a path through a maze of a given size. The traditional ways of finding a path through a maze employ recursive algorithms in which unwanted or non-paths are eliminated in a recursive manner. Neural networks with their parallel and distributed nature of processing seem to provide a natural solution to this problem. We present a biologically inspired solution using a two level hierarchical neural network for the mapping of the maze as also the generation of the path if it exists. For a maze of size S the amount of time it takes would be a function of S (O(S)) and a shortest path (if more than one path exists) could be found in around S cycles where each cycle involves all the neurons doing their processing in a parallel manner. The solution presented in this paper finds all valid paths and a simple technique for finding the shortest path amongst them is also given. The results are very encouraging and more applications of the network setup used in this report are currently being investigated. These include synthetic modeling of biological neural mechanisms, traversal of decision trees, modeling of associative neural networks (as in relating visual and auditory stimuli of a given phenomenon) and surgical micro-robot trajectory planning and execution.     

Diagnosing diabetes using neural networks on small mobile devices

Pervasive computing is often mentioned in the context of improving healthcare. This paper presents a novel approach for diagnosing diabetes using neural networks and pervasive healthcare computing technologies. The recent developments in small mobile devices and wireless communications provide a strong motivation to develop new software techniques and mobile services for pervasive healthcare computing. A distributed end-to-end pervasive healthcare system utilizing neural network computations for diagnosing illnesses was developed. This work presents the initial results for a simple client (patient’s PDA) and server (powerful desktop PC) two-tier pervasive healthcare architecture. The computations of neural network operations on both client and server sides and wireless network communications between them are optimized for real time use of pervasive healthcare services.     

Developing an artificial retina by evolutionary cellular automata and self-organizing neural networks

Retinas are very important for human beings to get information about their environment. In this paper, we propose a new method to build artificial retinas which have many features similar to real ones. We use evolutionary cellular automata to extract some basic characteristics of objects, and use self-organizing neural networks to distinguish different objects. The results indicate a way to get computer vision by artificial life. This work was presented, in part, at the Third International Symposium on Artificial Life and Robotics, Oita, Japan, Janaury 19–21, 1998     

Stock market prediction using artificial neural networks with optimal feature transformation

This paper compares a feature transformation method using a genetic algorithm (GA) with two conventional methods for artificial neural networks (ANNs). In this study, the GA is incorporated to improve the learning and generalizability of ANNs for stock market prediction. Daily predictions are conducted and prediction accuracy is measured. In this study, three feature transformation methods for ANNs are compared. Comparison of the results achieved by a feature transformation method using the GA to the other two feature transformation methods shows that the performance of the proposed model is better. Experimental results show that the proposed approach reduces the dimensionality of the feature space and decreases irrelevant factors for stock market prediction.     

基于VLRBP神经网络的汇率预测

为了提高汇率预测的准确性,分别使用VLRBP神经网络模型和GRNN模型及ARIMA模型对欧元汇率时间序列进行建模和预测,通过实证分析发现基于VLRBP的神经网络对于含有大量非线性成分的欧元汇率时间序列的预测比较准确.在分析了最速下降BP学习算法的缺点后,提出利用VLRBP学习算法来解决神经网络振荡和收敛速度过慢的缺陷,并取得较好的效果.同时,为了提高VLRBP网络的泛化性能,提出在训练VLRBP神经网络时应用浴盆曲线方法选取隐层神经元个数和滑动窗口尺寸,试验结果表明该方法适合神经网络模型.     

Non-linear variable selection for artificial neural networks using partial mutual information

Artificial neural networks (ANNs) have been widely used to model environmental processes. The ability of ANN models to accurately represent the complex, non-linear behaviour of relatively poorly understood processes makes them highly suited to this task. However, the selection of an appropriate set of input variables during ANN development is important for obtaining high-quality models. This can be a difficult task when considering that many input variable selection (IVS) techniques fail to perform adequately due to an underlying assumption of linearity, or due to redundancy within the available data.This paper focuses on a recently proposed IVS algorithm, based on estimation of partial mutual information (PMI), which can overcome both of these issues and is considered highly suited to the development of ANN models. In particular, this paper addresses the computational efficiency and accuracy of the algorithm via the formulation and evaluation of alternative techniques for determining the significance of PMI values estimated during selection. Furthermore, this paper presents a rigorous assessment of the PMI-based algorithm and clearly demonstrates the superior performance of this non-linear IVS technique in comparison to linear correlation-based techniques.     

Mapping tropical forest structure in southeastern Madagascar using remote sensing and artificial neural networks

Tropical forest condition has important implications for biodiversity, climate change and human needs. Structural features of forests can serve as useful indicators of forest condition and have the potential to be assessed with remotely sensed imagery, which can provide quantitative information on forest ecosystems at high temporal and spatial resolutions. Herein, we investigate the utility of remote sensing for assessing, predicting and mapping two important forest structural features, stem density and basal area, in tropical, littoral forests in southeastern Madagascar. We analysed the relationships of basal area and stem density measurements to the Normalised Difference Vegetation Index (NDVI) and radiance measurements in bands 3, 4, 5 and 7 from the Landsat Enhanced Thematic Mapper Plus (ETM+). Strong relationships were identified among all of the individual bands and field based measurements of basal area (p<0.01) while there were weak and insignificant relationships among spectral response and stem density measurements. NDVI was not significantly correlated with basal area but was strongly and significantly correlated with stem density (r=−0.69, p<0.01) when using a subset of the data, which represented extreme values. We used an artificial neural network (ANN) to predict basal area from radiance values in bands 3, 4, 5 and 7 and to produce a predictive map of basal area for the entire forest landscape. The ANNs produced strong and significant relationships between predicted and actual measures of basal area using a jackknife method (r=0.79, p<0.01) and when using a larger data set (r=0.82, p<0.01). The map of predicted basal area produced by the ANN was assessed in relation to a pre-existing map of forest condition derived from a semi-quantitative field assessment. The predictive map of basal area provided finer detail on stand structural heterogeneity, captured known climatic influences on forest structure and displayed trends of basal area associated with degree of human accessibility. These findings demonstrate the utility of ANNs for integrating satellite data from the Landsat ETM+ spectral bands 3, 4, 5 and 7 with limited field survey data to assess patterns in basal area at the landscape scale.     

最佳拟合与神经网络相结合实现传感器特性线性化

提出了一种传感器特性线性化的方法.该方法把传感器特性分为线性和非线性段,用一种改进的BP神经网络映射传感器特性非线性段的反函数作为校正环节,用最佳拟合方法得到线性段的直线方程,从而实现传感器特性的线性化.经过仿真试用表明,这种方法可使传感器的非线性误差减小近十倍.最后,给出了一些仿真实验和仿真结果.     

Hybrid control of the three phase induction machine using artificial neural networks and fuzzy logic

Nowadays, the microcomputer performs calculations at an incredibly high rate of billions of instructions per second. That represents an exponential increase in the processing speed since the early days of the computer development, eventhough such growth did not show complex reasoning that even the simple biological organisms can make. The artificial intelligence techniques as an attempt to work about those limitations, are a promising alternative.Each intelligent technique has its particular strengths and weaknesses and cannot be universally implemented to any problem. Mixed together, these techniques can improve the solutions quality and allow application to various tasks. It is the reason why the AI is used increasingly in order to solve complex problems in engineering. Where, it is still necessary to make progress in the controller tuning.The idea proposed in this paper is simple and original. It is the result of a study that compared the performances of two controls based on the artificial intelligence techniques: the artificial neural networks and the fuzzy logic. The control proposed in this paper combines in a different manner these two techniques in the form of a hybrid control. The aim is to benefit from performances of each of these techniques, by using them in the same control block at the most suitable place.The performances of the this proposed hybrid control; applied to the three-phase induction motor supplied by voltage source inverter; are investigated and compared to those obtained from the controls based on artificial neural networks; fuzzy logic and conventional techniques. The results of simulation show the feasibility and the good performances achieved by the proposed control.     

Investigation of pollution flashover on high voltage insulators using artificial neural network

High voltage insulators form an essential part of the high voltage electric power transmission systems. Any failure in the satisfactory performance of high voltage insulators will result in considerable loss of capital, as there are numerous industries that depend upon the availability of an uninterrupted power supply. The importance of the research on insulator pollution has been increased considerably with the rise of the voltage of transmission lines. In order to determine the flashover behavior of polluted high voltage insulators and to identify to physical mechanisms that govern this phenomenon, the researchers have been brought to establish a modeling. Artificial neural networks (ANN) have been used by various researches for modeling and predictions in the field of energy engineering systems. In this study, model of V_C = f (H, D, L, σ, n, d) based on ANN which compute flashover voltage of the insulators were performed. This model consider height (H), diameter (D), total leakage length (L), surface conductivity (σ) and number of shed (d) of an insulator and number of chain (n) on the insulator.     

An investigation of artificial neural networks based prediction systems in software project management

A critical issue in software project management is the accurate estimation of size, effort, resources, cost, and time spent in the development process. Underestimates may lead to time pressures that may compromise full functional development and the software testing process. Likewise, overestimates can result in noncompetitive budgets. In this paper, artificial neural network and stepwise regression based predictive models are investigated, aiming at offering alternative methods for those who do not believe in estimation models. The results presented in this paper compare the performance of both methods and indicate that these techniques are competitive with the APF, SLIM, and COCOMO methods.     

利用人工鱼群算法优化前向神经网络

人工鱼群算法(AFSA)是一种最新提出的新型的寻优策略，文中尝试将人工鱼群算法用于三层前向神经网络的训练过程，建立了相应的优化模型，进行了实际的编程计算，并与加动量项的BP算法、演化算法以及模拟退火算法进行比较，结果表明AFSA具有鲁棒性强，全局收敛性好，以及对初值的不敏感性等特点。     

Reverse analysis via efficient artificial neural networks based on simulated Berkovich indentation considering effects of friction

Instrumented indentation test has become a popular method for characterization of materials of small volume such as those constitute the micro-electro-mechanical devices, micro-electronic packages and thin film. Berkovich indenter is one of the most popular indenter tips employed in the tests. The present study involves the finite element simulation of indentation by Berkovich-family of indenters to establish the load-displacement relations for elasto-plastic materials obeying power law. Effects of friction at the contact surfaces, which have been ignored by most of the researchers are considered in the analyses. Extensive 3-dimensional finite element analyses covering a wide practical range of materials have been carried out and the results adopted for material characterization via artificial neural network model based on an efficient reverse analysis algorithm. Direct mapping of the characteristics of the indentation curves to the material properties are performed and the characteristics of the network model deliberated. The tuned network can then be adopted to predict the mechanical properties of a new set of materials of small volume in micro-electro-mechanical components.     

Modeling plasma surface modification of textile fabrics using artificial neural networks

In this paper, a neural network approach is used to understand the effects of fabric features and plasma processing parameters on fabric surface wetting properties. In this approach, fourteen features characterizing woven structures and two plasma parameters are taken as input variables, and the water contact angle cosine and the capillarity height of woven fabrics as output variables. In order to reduce the complexity of the model and effectively learn the network structure from a small number of data, a fuzzy logic based method is used for selecting the most relevant parameters which are taken as input variables of the reduced neural network models. With these relevant parameters, we can effectively control the plasma treatment by selecting the most appropriate fabric materials. Two techniques are used for improving the generalization capability of neural networks: (i) early stopping and (ii) Bayesian regularization. A methodology for optimizing such models is described. The learning abilities and prediction capabilities of the neural net models are compared in terms of different statistical performance criteria. Moreover, a connection weight method is used to determine the relative importance of each input variable in the networks. The obtained results show that neural network models could predict the process performance with reasonable accuracy. However, the neural model trained using Bayesian regularization provides the best results. Thus, it can be concluded that Bayesian network promises to be a valuable quantitative tool to evaluate, understand, and predict woven fabric surface modification by atmospheric air-plasma treatment.     

基于人工神经网络的主机安全量化评估研究

基于人工神经网络的方法对主机安全性能进行量化评估。分析了BP人工神经网络模型的网络结构及学习算法,分析了影响目标主机安全性能的可能因素,并应用BP神经网络模型对目标主机的安全性能进行样本训练及实际测试。基于人工神经网络的主机安全量化评估为评价目标主机的安全性能提供了可行的方法。     

Improvement of time forecasting models using a novel hybridization of bootstrap and double bootstrap artificial neural networks

Hybrid models such as the Artificial Neural Network-Autoregressive Integrated Moving Average (ANN–ARIMA) model are widely used in forecasting. However, inaccuracies and inefficiency remain in evidence. To yield the ANN–ARIMA with a higher degree of accuracy, efficiency and precision, the bootstrap and the double bootstrap methods are commonly used as alternative methods through the reconstruction of an ANN–ARIMA standard error. Unfortunately, these methods have not been applied in time series-based forecasting models. The aims of this study are twofold. First, is to propose the hybridization of bootstrap model and that of double bootstrap mode called Bootstrap Artificial Neural Network-Autoregressive Integrated Moving Average (B-ANN–ARIMA) and Double Bootstrap Artificial Neural Network-Autoregressive Integrated Moving Average (DB-ANN–ARIMA), respectively. Second, is to investigate the performance of these proposed models by comparing them with ARIMA, ANN and ANN–ARIMA. Our investigation is based on three well-known real datasets, i.e., Wolf’s sunspot data, Canadian lynx data and, Malaysia ringgit/United States dollar exchange rate data. Statistical analysis on SSE, MSE, RMSE, MAE, MAPE and VAF is then conducted to verify that the proposed models are better than previous ARIMA, ANN and ANN–ARIMA models. The empirical results show that, compared with ARIMA, ANNs and ANN–ARIMA models, the proposed models generate smaller values of SSE, MSE, RMSE, MAE, MAPE and VAF for both training and testing datasets. In other words, the proposed models are better than those that we compare with. Their forecasting values are closer to the actual values. Thus, we conclude that the proposed models can be used to generate better forecasting values with higher degree of accuracy, efficiency and, precision in forecasting time series results becomes a priority.     

A generic fuzzy aggregation operator: rules extraction from and insertion into artificial neural networks

Multilayered feedforward artificial neural networks (ANNs) are black boxes. Several methods have been published to extract a fuzzy system from a network, where the input–output mapping of the fuzzy system is equivalent to the mapping of the ANN. These methods are generalized by means of a new fuzzy aggregation operator. It is defined by using the activation function of a network. This fact lets to choose among several standard aggregation operators. A method to extract fuzzy rules from ANNs is presented by using this new operator. The insertion of fuzzy knowledge with linguistic hedges into an ANN is also defined thanks to this operator.