Climate-driven latitudinal shift in fishing ground of jumbo flying squid (Dosidicus gigas) in the Southeast Pacific Ocean off Peru

The jumbo flying squid Dosidicus gigas is a pelagic squid species extensively distributed in the Eastern Pacific Ocean with climate-related geographical variability. An analysis was carried out to evaluate impacts of climatic and oceanographic variability on spatial distribution of D. gigas in the Southeast Pacific Ocean off Peru. Logbook data of the 2006–2013 Chinese squid-jigging fishery were used to determine latitudinal gravity centres (LATG) of fishing ground of D. gigas in relation to sea surface temperature (SST), chlorophyll-a (chl-a) concentration and sea surface height (SSH), coupled with the SST anomaly (SSTA) in the Niño 1 + 2 region. Results indicated that the SSTA in the Niño 1 + 2 region played crucial influences on SST, chl-a and SSH on the fishing ground of D. gigas. The LATG of D. gigas exhibited seasonal and interannual variability with closely associations with SST, chl-a, and SSH. Significantly positive relationships were found between monthly LATG and the average latitude of the most favourable contour lines of SST, chl-a, and SSH for D. gigas, with time lags at 0, 7, and 0 month, respectively. The spatial pattern of LATG largely responded to climate-induced oceanographic variability on the squid fishing ground: the Niño 1 + 2 SSTA became warm, the most favourable SST and SSH contour lines for D. gigas would move southward, resulting in a southward movement of the LATG; however, the Niño 1 + 2 SSTA shifted into cold episodes, the most favourable SST and SSH contour lines for D. gigas would shift northward, leading to a northward shift of the LATG. Our findings suggested that the SSTA in the Niño 1 + 2 region coupled with the most favourable contour lines of SST and SSH were the major drivers regulating the latitudinal movement of fishing ground of D. gigas in the Southeast Pacific Ocean off Peruvian waters.     

基于卷积神经网络的遥感图像分类研究

遥感图像分类是模式识别技术在遥感领域的具体应用,针对遥感图像处理中的分类问题,提出了一种基于卷积神经网络(convolutional neural networks,CNN)的遥感图像分类方法,并针对单源特征无法提供有效信息的问题,设计了一种多源多特征融合的方法,将遥感图像的光谱特征、纹理特征、空间结构特征等按空间维度以向量或矩阵的形式进行有效融合,以此训练CNN模型。实验表明,多源多特征相融合能够加快模型收敛速度,有效提高遥感图像的分类精度;与其他分类方法相比,CNN能够取得更高的分类精度,获得更优的分类效果。     

Estimation of all-terminal network reliability using an artificial neural network

The exact calculation of all-terminal network reliability is an NP-hard problem, with computational effort growing exponentially with the number of nodes and links in the network. During optimal network design, a huge number of candidate topologies are typically examined with each requiring a network reliability calculation. Because of the impracticality of calculating all-terminal network reliability for networks of moderate to large size, Monte Carlo simulation methods to estimate network reliability and upper and lower bounds to bound reliability have been used as alternatives. This paper puts forth another alternative to the estimation of all-terminal network reliability — that of artificial neural network (ANN) predictive models. Neural networks are constructed, trained and validated using the network topologies, the link reliabilities, and a network reliability upperbound as inputs and the exact network reliability as the target. A hierarchical approach is used: a general neural network screens all network topologies for reliability followed by a specialized neural network for highly reliable network designs. Both networks with identical link reliability and networks with varying link reliability are studied. Results, using a grouped cross-validation approach, show that the ANN approach yields more precise estimates than the upperbound, especially in the worst cases. Using the reliability estimation methods of the ANN, the upperbound and backtracking, optimal network design by simulated annealing is considered. Results show that the ANN regularly produces superior network designs at a reasonable computational cost.Scope and purposeAn important application area of operations research is the design of structures, products or systems where both technical and business aspects must be considered. One expanding design domain is the design of computer or communications networks. While cost is a prime consideration, reliability is equally important. A common reliability measure is all-terminal reliability, the probability that all nodes (computers or terminals) on the network can communicate with all others. Exact calculation of all-terminal reliability is an NP-hard problem, precluding its use during optimal network topology design, where this calculation must be made thousands or millions of times. This paper presents a novel computationally practical method for estimating all-terminal network reliability. Is shown how a neural network can be used to estimate all-terminal network reliability by using the network topology, the link reliabilities and an upperbound on all-terminal network reliability as inputs. The neural network is trained and validated on a very minute fraction of possible network topologies, and once trained, it can be used without restriction during network design for a topology of a fixed number of nodes. The trained neural network is extremely fast computationally and can accommodate a variety of network design problems. The neural network approach, an upper bound approach and an exact backtracking calculation are compared for network design using simulated annealing for optimization and show that the neural network approach yields superior designs at manageable computational cost.     

Using remote-sensing data to detect habitat suitability for yellowfin tuna in the Western and Central Pacific Ocean

The empirical habitat suitability index (HSI) has been widely used to examine the habitat characteristics of terrestrial animals, though rarely used in highly migratory fish such as tuna. This study used the geographic information system technique to establish empirical models of HSI for yellowfin tuna (YFT) in the Western and Central Pacific Ocean (WCPO). Daily catch data from the Taiwanese purse seine fishery during 2003–2007 were aggregated monthly into sequential degrees before match processing the conducted data to obtain monthly remote-sensing data for multi-environmental factors, including sea surface temperature (SST), chlorophyll-a (chl-a), sea surface height (SSH) and sea surface salinity (SSS). According to the frequency distribution of each factor on which YFT were caught, this study transformed the values of the four factors into a suitability index (SI) ranging from low to high (0–1). These SI values were consequently combined into different empirical HSI models, and the optimum models were selected using the general linear model. The optimum empirical HSI for YFT in the study area was converted for SI (SST, SSH, chl-a and SSS) using the arithmetic mean model, of which the correct prediction rate was 71.9%. An agreement was present between the average HSI and total YFT catch. Furthermore, the high HSI area corresponds with the displacement of catch per unit effort (CPUE).     

Noise removal in electroencephalogram signals using an artificial neural network based on the simultaneous perturbation method

Electroencephalogram (EEG) recordings often experience interference by different kinds of noise, including white, muscle and baseline, severely limiting its utility. Artificial neural networks (ANNs) are effective and powerful tools for removing interference from EEGs. Several methods have been developed, but ANNs appear to be the most effective for reducing muscle and baseline contamination, especially when the contamination is greater in amplitude than the brain signal. An ANN as a filter for EEG recordings is proposed in this paper, developing a novel framework for investigating and comparing the relative performance of an ANN incorporating real EEG recordings. This method is based on a growing ANN that optimized the number of nodes in the hidden layer and the coefficient matrices, which are optimized by the simultaneous perturbation method. The ANN improves the results obtained with the conventional EEG filtering techniques: wavelet, singular value decomposition, principal component analysis, adaptive filtering and independent components analysis. The system has been evaluated within a wide range of EEG signals. The present study introduces a new method of reducing all EEG interference signals in one step with low EEG distortion and high noise reduction.     

Dynamics modeling of robotic manipulators using an artificial neural network

Dynamics modeling is important for the design, analysis, simulation, and control of robotic and other computer-controlled mechanical systems. The complete dynamic modeling of such systems involves the computationally intensive solution of a set of non-linear, coupled differential equations. Artificial neural networks are well suited for this application due to their ability to represent complex functions and, potentially, to operate in real time. The application of an artificial neural network to dynamics modeling of robotic systems is investigated. The Cerebellar Model Arithmetic Computer (CMAC) is employed. A hybrid implementation of CMAC is proposed to allow use of the model for either simulation or control of robotic manipulators. The success of the simulated results and the accuracy of the generated outputs after a few training cycles demonstrate great promise for further development of the method and its implementation in control systems. © 1994 John Wiley & Sons, Inc.     

A data clustering algorithm for stratified data partitioning in artificial neural network

The statistical properties of training, validation and test data play an important role in assuring optimal performance in artificial neural networks (ANNs). Researchers have proposed optimized data partitioning (ODP) and stratified data partitioning (SDP) methods to partition of input data into training, validation and test datasets. ODP methods based on genetic algorithm (GA) are computationally expensive as the random search space can be in the power of twenty or more for an average sized dataset. For SDP methods, clustering algorithms such as self organizing map (SOM) and fuzzy clustering (FC) are used to form strata. It is assumed that data points in any individual stratum are in close statistical agreement. Reported clustering algorithms are designed to form natural clusters. In the case of large multivariate datasets, some of these natural clusters can be big enough such that the furthest data vectors are statistically far away from the mean. Further, these algorithms are computationally expensive as well. We propose a custom design clustering algorithm (CDCA) to overcome these shortcomings. Comparisons are made using three benchmark case studies, one each from classification, function approximation and prediction domains. The proposed CDCA data partitioning method is evaluated in comparison with SOM, FC and GA based data partitioning methods. It is found that the CDCA data partitioning method not only perform well but also reduces the average CPU time.     

Evolution of an artificial neural network based autonomous landvehicle controller

This paper presents an evolutionary method for creating an artificial neural network based autonomous land vehicle controller. The evolved controllers perform better in unseen situations than those trained with an error backpropagation learning algorithm designed for this task. In this paper, an overview of the previous connectionist based approaches to this task is given, and the evolutionary algorithms used in this study are described in detail. Methods for reducing the high computational costs of training artificial neural networks with evolutionary algorithms are explored. Error metrics specific to the task of autonomous vehicle control are introduced; the evolutionary algorithms guided by these error metrics reveal improved performance over those guided by the standard sum-squared error metric. Finally, techniques for integrating evolutionary search and error backpropagation are presented. The evolved networks are designed to control Carnegie Mellon University's NAVLAB vehicles in road following tasks.     

An incremental-learning neural network for the classification of remote-sensing images

A novel classifier for the analysis of remote-sensing images is proposed. Such a classifier is based on Radial Basis Function (RBF) neural networks and relies on an incremental-learning technique. This technique allows the periodical acquisition of new information whenever a new training set becomes available, while preserving the knowledge learnt by the network on previous training sets. In addition, in each retraining phase, the network architecture is automatically updated so that new classes may be considered. These characteristics make the proposed neural classifier a promising tool for several remote-sensing applications.     

A hybrid method for hydroelectric generation scheduling using an artificial neural network

This paper presents a decomposition method for finding an optimal operating policy of interconnected hydroelectric power plants using an artificial neural network. The coupling constraints on reservoir storage at the end of the planning horizon are relaxed using coordinating multipliers that result in interval wise decomposition of the overall problem. Resulting subproblems are solved sequentially, which reduces the complexity of the problem. Each subproblem is solved using a two-phase neural network approach. An efficient heuristic algorithm is developed to find the feasible solution. A case study considering scheduling of the Bhakra-Beas reservoir system is also presented in this paper. The new method demonstrates the potential of achieving an improved performance.     

Enhancement of OMI aerosol optical depth data assimilation using artificial neural network

A regional chemical transport model assimilated with daily mean satellite and ground-based aerosol optical depth (AOD) observations is used to produce three-dimensional distributions of aerosols throughout Europe for the year 2005. In this paper, the AOD measurements of the Ozone Monitoring Instrument (OMI) are assimilated with Polyphemus model. In order to overcome missing satellite data, a methodology for preprocessing AOD based on neural network (NN) is proposed. The aerosol forecasts involve two-phase process assimilation and then a feedback correction process. During the assimilation phase, the total column AOD is estimated from the model aerosol fields. The main contribution is to adjust model state to improve the agreement between the simulated AOD and satellite retrievals of AOD. The results show that the assimilation of AOD observations significantly improves the forecast for total mass. The errors on aerosol chemical composition are reduced and are sometimes vanished by the assimilation procedure and NN preprocessing, which shows a big contribution to the assimilation process.     

Integration of fuzzy neural network and artificial immune system-based back-propagation neural network for sales forecasting using qualitative and quantitative data

Sales forecasting plays a very important role in business operation. Many researches generally employ statistical methods, such as regression or auto-regressive integrated moving average model, to forecast the product sales. However, they only can consider the quantitative data. Some exogenous qualitative variables have more influence on forecasting result. Thus, this study attempts to propose a integrated forecasting system which is able to consider both quantitative and qualitative factors to achieve a more comprehensive result. Basically, fuzzy neural network is first employed to capture the expert knowledge regarding some qualitative factors. Then, it is combined with the time series data using an artificial immune system based back-propagation neural network. A laptop sales data set provided by a distributor in Taiwan is applied to verify the proposed approach. The computational result indicates that the proposed approach is superior to other forecasting methods. It can be used to decrease the inventory costs and enhance the customer satisfaction.     

Land cover classification in rugged areas using simulated moderateresolution remote sensor data and an artificial neural network

Rugged land cover classification accuracies produced by an artificial neural network (ANN) using simulated moderate-resolution remote sensor data exceed overall accuracies produced using the maximum likelihood rule (MLR). Land cover in spatially-complex areas and at broad spatial scales may be difficult to monitor due to ambiguities in spectral reflectance information produced from cloud-related and topographic effects, or from sampling constraints. Such ambiguities may produce inconsistent estimates of changes in vegetation status, surface energy balance, run-off yields, or other land cover characteristics. By use of a 'back-classification' protocol, which uses the same pixels for testing as for training the classifier, tests of ANN versus MLR-based classifiers demonstrated the ANNbased classifier equalled or exceeded classification accuracies produced by the MLR-based classifier in five of six land cover classes evaluated.     

基于人工神经网络的智能化煤矿监测系统的路径规划

给出了一种煤矿监测、数据采集用智能化监测系统的设计方案,监测系统在运行的过程中可实现数据采集、循迹、避障、测距等功能.监测系统的路径规划采用了一种人工神经网络算法,该算法利用神经网络模型描述监测系统工作空间的动态环境信息,并建立起机器人动态避障与网络输出间的关系.仿真结果表明该算法具有较好的环境适应性和实时性,所用的动态路径规划方法是正确和有效的.     

基于模糊神经网络势场法的机器人动态路径规划

针对机器人局部路径规划的特点和传统人工势场理论存在不足的问题,采用改进的斥力势场函数,将机器人与目标的相对距离和速度考虑在内以解决局部最小值问题。引入神经网络模糊系统,兼顾了系统的鲁棒性和快速性,并在应用实例中得到了有效的验证。     

Applying tree-based ensemble algorithms to the classification of ecological zones using multi-temporal multi-source remote-sensing data

The decision tree method has grown fast in the past two decades and its performance in classification is promising. The tree-based ensemble algorithms have been used to improve the performance of an individual tree. In this study, we compared four basic ensemble methods, that is, bagging tree, random forest, AdaBoost tree and AdaBoost random tree in terms of the tree size, ensemble size, band selection (BS), random feature selection, classification accuracy and efficiency in ecological zone classification in Clark County, Nevada, through multi-temporal multi-source remote-sensing data. Furthermore, two BS schemes based on feature importance of the bagging tree and AdaBoost tree were also considered and compared. We conclude that random forest or AdaBoost random tree can achieve accuracies at least as high as bagging tree or AdaBoost tree with higher efficiency; and although bagging tree and random forest can be more efficient, AdaBoost tree and AdaBoost random tree can provide a significantly higher accuracy. All ensemble methods provided significantly higher accuracies than the single decision tree. Finally, our results showed that the classification accuracy could increase dramatically by combining multi-temporal and multi-source data set.     

Classification of heart rate data using artificial neural network and fuzzy equivalence relation

The electrocardiogram is a representative signal containing information about the condition of the heart. The shape and size of the P-QRS-T wave, the time intervals between its various peaks, etc. may contain useful information about the nature of disease afflicting the heart. However, these subtle details cannot be directly monitored by the human observer. Besides, since bio-signals are highly subjective, the symptoms may appear at random in the time scale. Therefore, the signal parameters, extracted and analysed using computers, are highly useful in diagnostics. This paper deals with the classification of certain diseases using artificial neural network (ANN) and fuzzy equivalence relations. The heart rate variability is used as the base signal from which certain parameters are extracted and presented to the ANN for classification. The same data is also used for fuzzy equivalence classifier. The feedforward architecture ANN classifier is seen to be correct in about 85% of the test cases, and the fuzzy classifier yields correct classification in over 90% of the cases.     

基于ANN伪量测建模的配电网状态估计

针对配电网状态估计实时量测数量的不足,提出了一种基于ANN伪量测建模的配电网状态估计算法。该方法采用人工神经网络网络（ANN）,将部分实时量测数据作为神经网络的输入,产生较为精确的负荷伪量测数据。此外,应用高斯混合模型对产生伪量测的误差进行分解拟合,从而获得负荷伪量测的权重。最后,将获得的伪量测及其权重输入到状态估计模块中,实现了配电网的状态估计。通过英国标准配网系统（UKGDS）中16节点模型的仿真结果表明,该算法提高了配电网状态估计的精度,具有一定的现实意义和理论价值。     

Automated tuning of an electronic circuit board using the artificial neural network approach

Manufacturing of electronic circuits for microwave communication boards often requires tuning of different circuit characteristics by manual adjustment of several trimmer components, including the trimmer's resistance and capacitance. This manual tuning process was automated by applying the artificial neural network modeling approach. In the considered tuning process, which required manual adjustment of a set of trimmers, multiple specification criteria had to be satisfied by several trimmer rotations. The tuning process was described in terms of three independent steps: the circuit output measurement, trimmer selection, and trimmer rotation. The trimmer selection was performed by a semi-supervised neural network, which learned the patterns of circuit characteristics and the deviations between the ideal and practical outputs. Another network was developed for determination of trimmer rotation rate. The results, based on computer simulation of the tuning process, showed that the developed system improved performance of the tuning process, allowing for automation of the microwave circuit board tuning task in a real manufacturing environment.