城市边缘区景观生态规划的人工神经网络模型

景观生态规划是景观生态学的一个重要应用领域,本文在地理信息系统的辅助下引入了人工神经网络这一新兴应用技术,建立了城市边缘区景观生态规划的BP神经网络模型,模型以区域的高程、高程离差、坡度、坡度离差、地貌分区、离黄河距离、居民点数七个要素作为输入变量,选取斑块密度、分维数、Shannon多样性指数和聚集度指数作为输出变量,精心采集了20个样本对网络进行训练,结果表明网络收敛效果理想,泛化能力强,为景观生态规划提供了一个新的模拟分析手段。     

Growing of a Fuzzy Recurrent Artificial Neural Network (FRANN) for pattern classification

This paper describes a method for growing a recurrent neural network of fuzzy threshold units for the classification of feature vectors. Fuzzy networks seem natural for performing classification, since classification is concerned with set membership and objects generally belonging to sets of various degrees. A fuzzy unit in the architecture proposed here determines the degree to which the input vector lies in the fuzzy set associated with the fuzzy unit. This is in contrast to perceptrons that determine the correlation between input vector and a weighting vector. The resulting membership value, in the case of the fuzzy unit, is compared with a threshold, which is interpreted as a membership value. Training of a fuzzy unit is based on an algorithm for linear inequalities similar to Ho-Kashyap recording. These fuzzy threshold units are fully connected in a recurrent network. The network grows as it is trained. The advantages of the network and its training method are: (1) Allowing the network to grow to the required size which is generally much smaller than the size of the network which would be obtained otherwise, implying better generalization, smaller storage requirements and fewer calculations during classification; (2) The training time is extremely short; (3) Recurrent networks such as this one are generally readily implemented in hardware; (4) Classification accuracy obtained on several standard data sets is better than that obtained by the majority of other standard methods; and (5) The use of fuzzy logic is very intuitive since class membership is generally fuzzy.     

A Functionally Equivalent Artificial Neural Network Model of the Prey Orientation Behavior of Waterstriders (Gerridae)

Waterstriders, a family (Heteroptera, Gerridae) of predacious insects, orient toward the source of water surface vibrations. We describe an artificial neural network that simulates a waterstrider's discrete rotational movement towards a prey item and compare the results to published data. A back-propagation network with six input units, each corresponding to a vibration receptor on a leg of the waterstrider, and two output units corresponding to the elicited angle of rotation, was used. The network was trained with a full complement of receptors to rotate towards the point source of a surface vibration. When the network was tested with all receptors present, a linear relationship was found between the desired and obtained rotational angles. Lesioning of one or two receptors resulted in marked deviation from linearity within the angular range of detection corresponding to that of the amputated receptor(s), while amputation of three receptors resulted in the network rotating contralaterally to all vibrations originating ipsolaterally to the lesioned side. All trials produced results that corresponded qualitatively to published behavioral data.     

BP人工神经网络在光谱定量预测中的应用

人工神经网络是模仿大脑神经元网络结构和功能而建立的一种信息处理系统,广泛的应用于各种波谱数据处理。误差反向传播多层前馈式网络(back-propagation network,简称BP网络)应用最广,发展最为迅速。将BP神经网络用于紫外-可见吸收光谱和拉曼光谱数据的定量分析和预测,与原文的一元线性回归模型数据处理方法相比,获得了比较满意的预测结果,预测精度有显著提高。这为相关的光谱分析和数据处理提供了一种更有效、更精确的方法。     

Artificial Neural Network Method for Predicting the Specificity of GalNAc-transferase

The specificity of GalNAc-transferase is consistent with the existence of an extended site composed of nine subsites, denoted by R₄, R₃, R₂, R₁, R₀, R₁, R₂, R₃, and R₄, where the acceptor at R₀ is either Ser or Thr to which the reducing monosaccharide is anchored. To predict whether a peptide will react with the enzyme to form a Ser- or Thr-conjugated glycopeptide, a neural network method—Kohonen's self-organization model is proposed in this paper. Three hundred five oligopeptides are chosen for the training site, with another 30 oligopeptides for the test set. Because of its high correct prediction rate (26/30=86.7%) and stronger fault-tolerant ability, it is expected that the neural network method can be used as a technique for predicting O-glycosylation and designing effective inhibitors of GalNAc-transferase. It might also be useful for targeting drugs to specific sites in the body and for enzyme replacement therapy for the treatment of genetic disorders.     

A geomorphological model of intertidal cove marshes with application to wetlands management

Detailed topographic and hydrologic surveys were conducted in five intertidal cove marshes in an outer coastal plain landscape to test the hypothesis that the equilibrium geologic state of intertidal habitats residing in similar landscape situations conforms to a consistent geometric form. The equation V=1571.84A^1.70 (R²=96.2%) describes the relationship between hectares of marsh (A) and cubic meter volume at mean high tide (V). An empirical relationship between tide height and volume was found to obey the power series V_p=L^2.38 (R²=99.6%), where V_p is volume as a percent of full pool and L is water height as a percent of mean high tide. A dimensionless index describing the relationship between area and volume is consistent for each marsh and approaches 0.10. A channel form parameter describing width to channel depth ratios is of consistent value for four of the five marshes. These provide evidence of deterministic rather than stochastic geologic development. The benefits of applying natural basin shape patterns in the design and engineering of created/restored intertidal marshes are highlighted and a generic basin is modeled (based on the geometrical section of a paraboloid retained by simple integration) as an example of the potential applicability of the study.     

ANNA – Artificial Neural Network model for predicting species abundance and succession of blue-green algae

Predictive potential of deductive and inductivephytoplankton models are compared regarding theirusefulness for forecasting and control of harmfulalgal blooms. While applications of deductive modelsstill seem to be restricted by lack of knowledge, ad hocinductive models sometimes prove to bestraightforward and useful. The inductive neuralnetwork model ANNA is documented by means of anapplication to Lake Kasumigaura, Japan. ANNA wasvalidated for five blue-green algae species wherepredictive accuracy has improved with increased eventand time resolution of training data. A scenarioanalysis on species succession has demonstrated thepotential of ANNA for hypothesis testing. Finally,implications for use of ANNA for operational algalbloom control are discussed.     

Artificial Neural Network Inference (ANNI): A Study on Gene-Gene Interaction for Biomarkers in Childhood Sarcomas

Objective

To model the potential interaction between previously identified biomarkers in children sarcomas using artificial neural network inference (ANNI).

Method

To concisely demonstrate the biological interactions between correlated genes in an interaction network map, only 2 types of sarcomas in the children small round blue cell tumors (SRBCTs) dataset are discussed in this paper. A backpropagation neural network was used to model the potential interaction between genes. The prediction weights and signal directions were used to model the strengths of the interaction signals and the direction of the interaction link between genes. The ANN model was validated using Monte Carlo cross-validation to minimize the risk of over-fitting and to optimize generalization ability of the model.

Results

Strong connection links on certain genes (TNNT1 and FNDC5 in rhabdomyosarcoma (RMS); FCGRT and OLFM1 in Ewing’s sarcoma (EWS)) suggested their potency as central hubs in the interconnection of genes with different functionalities. The results showed that the RMS patients in this dataset are likely to be congenital and at low risk of cardiomyopathy development. The EWS patients are likely to be complicated by EWS-FLI fusion and deficiency in various signaling pathways, including Wnt, Fas/Rho and intracellular oxygen.

Conclusions

The ANN network inference approach and the examination of identified genes in the published literature within the context of the disease highlights the substantial influence of certain genes in sarcomas.     

MiRANN: a reliable approach for improved classification of precursor microRNA using Artificial Neural Network model

MicroRNA (miRNA) is a special class of short noncoding RNA that serves pivotal function of regulating gene expression. The computational prediction of new miRNA candidates involves various methods such as learning methods and methods using expression data. This article has proposed a reliable model - miRANN which is a supervised machine learning approach. MiRANN used known pre-miRNAs as positive set and a novel negative set from human CDS regions. The number of known miRNAs is now huge and diversified that could cover almost all characteristics of unknown miRNAs which increases the quality of the result (99.9% accuracy, 99.8% sensitivity, 100% specificity) and provides a more reliable prediction. MiRANN performs better than other state-of-the-art approaches and declares to be the most potential tool to predict novel miRNAs. We have also tested our result using a previous negative set. MiRANN, opens new ground using ANN for predicting pre-miRNAs with a promise of better performance.     

Monte Carlo simulation in phylogenies: An application to test the constancy of evolutionary rates

Monte Carlo simulation has commonly been used in phylogenetic studies to test different tree-reconstruction methods, and consequently, its application for testing evolutionary models can be considered as a natural extension of this usage. Repetitive simulation of a given evolutionary process, under the restrictions imposed by the model to be tested, along a determinate tree topology allow the estimate of probability distributions for the desired parameters. Next, the phylogenetic tree can be reconstructed again without the constraints of the model, and the parameter of interest, derived from this tree, can be compared to the corresponding probability distribution derived from the restricted, simulated trees. As an example we have used Monte Carlo simulation to test the constancy of evolutionary rates in a set of cytochrome-c protein sequences. Correspondence to: J. Dopazo     

An Artificial Neural Network Model to Predict the Phenology of Early-Maturing Soybean Varieties from Climatic Factors

Biophysics - Abstract—Soybean phenology is strongly influenced by temperature and day length, and phenological records clearly reflect the changes in climatic conditions. A model including...     

运用BP人工神经网络预测长江中下游梨黑星病发病的研究

提出了运用人工神经网络技术进行梨黑星病预测的新思路,并以梨黑星病发病的主要影响因素,即上年7月的降水量和上年8月的降水量作为训练样本模式提供给网络,按照误差逆传播网络的学习规则对网络进行训练,经过计算机2844次学习后,网络达到预先给定的收敛标准,使网络具备了预测梨树黑星病流行趋势和流行强度的功能。检验结果表明,该方法性能良好,预测准确率高,可望成为果树病早害预测预报的有效辅助手段。     

Estimating fish community diversity from environmental features in the Tagus estuary (Portugal): Multiple Linear Regression and Artificial Neural Network approaches

Relationships between environmental variables and diversity (Shannon‐Weaver index) of the fish communities in the Tagus estuary and adjacent coastal areas were analyzed. The focus was on the linearity or nonlinearity of these abiotic/biotic characteristics, with the aim to obtain an accurate short–medium term time‐scale diversity prediction from habitat variables alone. Multiple Linear Regressions (MLR) were used for the linear approach and Artificial Neural Networks (ANNs) for the nonlinear approach. MLR results in the external validation phase indicated a lack of model accuracy (R² = 0.0710; %SEP = 47.5868; E = ?0.0217; ARV = 1.0217; N = 43). Results of the best of the Artificial Neural Networks used in this study (12‐15‐15‐1 architecture) in the external validation phase (ANN: R² = 0.9736; %SEP = 7.8499; E = 0.9722; ARV = 0.0278; N = 43) were more accurate than those obtained with MLR. This indicates a clear nonlinear relationship between variables. In the best ANN model, nitrate concentration, depth, dissolved oxygen and temperature were the most important predictors of fish diversity in the Tagus estuary. The sensibility analysis indicated that the remaining variables (silicate, nitrite, transparency, salinity, slope, phosphate, water particulate organic matter, and chlorophyll a) played lesser roles in the model.     

Prediction of Disease Causing Non-Synonymous SNPs by the Artificial Neural Network Predictor NetDiseaseSNP

We have developed a sequence conservation-based artificial neural network predictor called NetDiseaseSNP which classifies nsSNPs as disease-causing or neutral. Our method uses the excellent alignment generation algorithm of SIFT to identify related sequences and a combination of 31 features assessing sequence conservation and the predicted surface accessibility to produce a single score which can be used to rank nsSNPs based on their potential to cause disease. NetDiseaseSNP classifies successfully disease-causing and neutral mutations. In addition, we show that NetDiseaseSNP discriminates cancer driver and passenger mutations satisfactorily. Our method outperforms other state-of-the-art methods on several disease/neutral datasets as well as on cancer driver/passenger mutation datasets and can thus be used to pinpoint and prioritize plausible disease candidates among nsSNPs for further investigation. NetDiseaseSNP is publicly available as an online tool as well as a web service: http://www.cbs.dtu.dk/services/NetDiseaseSNP     

利用神经网络对蛋白质家族进行预测

提出了一种利用神经网络为蛋白质家族建立模型的方法,这一方法的理论出发点是利用神经网络从一组同家族蛋白质序列中识别出共同的特征模式,建好的模型可用于预测蛋白质家族,使用这一方法。所能识别的模式在长度、位点等方面都不受限制。而且建模及预测过程中输入神经网络的蛋白质序列不需要作预对齐。对Pfam蛋白质库中的二十个家族运用此方法,预测的平均正确率达到了95．5％。     

人工神经网络在糖尿病住院费用研究中的应用

目的 通过对糖尿病住院费用的主要影响因素进行分析,探寻有效分析方法,为合理控制糖尿病住院费用提供理论依据。方法 研究对象为北京市2011年13家三级甲等医院的全部2型糖尿病住院患者病案首页数据,诊断依据为出院主诊断（ICD编码为E11）。采用反向传播神经网络建模,并与多元线性回归模型相比较,分析糖尿病住院费用影响因素。结果 (1)反向传播神经网络预测误差比多元线性回归低,在数据拟合方面优于多元线性回归模型。(2)两模型结果显示,糖尿病住院费用影响因素中排前两位的是住院日和出院科别,单项费用中药品费、化验费、检查费占据前三位。结论 在对糖尿病住院费用分析时反向传播神经网络优于多元线性回归。     

An Artificial Neural Network Estimation of Gait Balance Control in the Elderly Using Clinical Evaluations

The use of motion analysis to assess balance is essential for determining the underlying mechanisms of falls during dynamic activities. Clinicians evaluate patients using clinical examinations of static balance control, gait performance, cognition, and neuromuscular ability. Mapping these data to measures of dynamic balance control, and the subsequent categorization and identification of community dwelling elderly fallers at risk of falls in a quick and inexpensive manner is needed. The purpose of this study was to demonstrate that given clinical measures, an artificial neural network (ANN) could determine dynamic balance control, as defined by the interaction of the center of mass (CoM) with the base of support (BoS), during gait. Fifty-six elderly adults were included in this study. Using a feed-forward neural network with back propagation, combinations of five functional domains, the number of hidden layers and error goals were evaluated to determine the best parameters to assess dynamic balance control. Functional domain input parameters included subject characteristics, clinical examinations, cognitive performance, muscle strength, and clinical balance performance. The use of these functional domains demonstrated the ability to quickly converge to a solution, with the network learning the mapping within 5 epochs, when using up to 30 hidden nodes and an error goal of 0.001. The ability to correctly identify the interaction of the CoM with BoS demonstrated correlation values up to 0.89 (P<.001). On average, using all clinical measures, the ANN was able to estimate the dynamic CoM to BoS distance to within 1 cm and BoS area to within 75 cm². Our results demonstrated that an ANN could be trained to map clinical variables to biomechanical measures of gait balance control. A neural network could provide physicians and patients with a cost effective means to identify dynamic balance issues and possible risk of falls from routinely collected clinical examinations.     

Artificial neural networks (ANN) approach for modeling of removal of Lanaset Red G on Chara contraria

A three-layer artificial neural network (ANN) was constructed to predict the removal efficiency of Lanaset Red (LR) G on Chara contraria based on 2304 experimental sets. The effects of operating variables (particle size, adsorbent dosage, pH regimes, dye concentration, and contact time) were studied to optimize the sorption conditions of this dye. The operating variables were used as the input to the constructed neural network to predict the dye uptake at any time as the output. This adsorbent was characterized by FTIR. Pseudo second-order model was also fitted to the experimental data. According to values of error analyses and determinations coefficient, the ANN was more appropriate to describe this adsorption process. Result of this model indicated that pH regimes had the highest importance effect (49%) on the dye uptake.