普陀山岛旅游生态安全发展趋势预测

科学地预测海岛目的地旅游生态安全发展趋势,对促进海岛旅游经济和生态环境协调发展具有重要的理论意义和实践价值。基于可持续发展的视角,建立了由承载力-支持力-吸引力-延续力和发展力(CSAED模型)子系统构成的普陀山旅游生态安全指标体系,并在灰色系统GM(1,1)模型和RBF神经网络模型比较选优的基础上,对普陀山岛旅游生态安全发展趋势进行了预测。研究结果表明:(1)和灰色系统GM(1,1)模型相比,RBF神经网络模型的Pearson相关系数和误差均方根值更优,可更精确地拟合普陀山岛旅游生态安全发展趋势;(2)2015—2020年,普陀山岛旅游生态安全指数的RBF神经网络模型预测结果由0.7017增加至0.8135,安全等级由比较安全上升至非常安全。研究结果可为维护普陀山岛旅游生态安全提供决策依据。     

An improved genetic algorithm based fuzzy-tuned neural network

This paper presents a fuzzy-tuned neural network, which is trained by an improved genetic algorithm (GA). The fuzzy-tuned neural network consists of a neural-fuzzy network and a modified neural network. In the modified neural network, a neuron model with two activation functions is used so that the degree of freedom of the network function can be increased. The neural-fuzzy network governs some of the parameters of the neuron model. It will be shown that the performance of the proposed fuzzy-tuned neural network is better than that of the traditional neural network with a similar number of parameters. An improved GA is proposed to train the parameters of the proposed network. Sets of improved genetic operations are presented. The performance of the improved GA will be shown to be better than that of the traditional GA. Some application examples are given to illustrate the merits of the proposed neural network and the improved GA.     

基于MODIS时间序列反射率数据的雷竹林LAI反演

本文以雷竹林为研究对象,基于MODIS地表反射率数据构建了归一化植被指数(NDVI)、比值植被指数(SR)、Gitelson绿色植被指数(GI)、增强型植被指数(EVI)和土壤调整植被指数(SAVI)5种植被指数,并将其与MODIS 7个波段原始反射率数据作为遥感变量,采用逐步回归和相关分析两种方法进行变量筛选,结合LAI实测数据构建了逐步回归和BP神经网络两种模型,对雷竹林生态系统观测站点2014年1月-2017年3月LAI时间系列数据进行反演,并将反演结果与同时期MOD15A2 LAI产品进行对比分析.结果表明: SR为唯一入选逐步回归模型的变量;b₁、b₂、b₃和b₇以及5种植被指数与LAI之间的相关性均达到显著水平,可作为BP神经网络模型的输入变量.使用BP神经网络反演得到的LAI与实测LAI之间的相关性显著,R²为0.71,RMSE为0.34,RMSE_r为13.6%,其R²比逐步回归模型提高了10.9%,RMSE降低了5.6%,RMSE_r降低了12.3%,与MODIS LAI相比,其R²提高了54.5%,RMSE降低了79.3%,RMSE_r降低了79.1%.结合MODIS时间序列反射率和BP神经网络模型能够精确地反演雷竹林LAI,为实现基于遥感技术快速监测区域雷竹林LAI提供可行的方法.     

Simulation of tagasaste pulping using soda-anthraquinone

In this work, published experimental result data of the pulping of tagasaste (Chamaecytisus proliferus L.F.) with soda and anthraquinone (AQ) have been used to develop a model using a neural network. The paper presents the development of a model with a neural network to predict the effects that the operational variables of the pulping reactor (temperature, soda concentration, AQ concentration, time and liquid/solid ratio) have on the properties of the paper sheets of the obtained pulp (brightness, traction index, burst index and tear index). Using a factorial experimental design, the results obtained with the neural network model are compared with those obtained from a polynomial model. The neural network model shows a higher prediction precision that the polynomial model.     

林地叶面积指数遥感估算方法适用分析

叶面积指数是与森林冠层能量和CO2交换密切相关的一个重要植被结构参数,为了探讨估算林地叶面积指数LAI的遥感适用方法和提高精度的途径,利用TRAC仪器测定北京城区森林样地的LAI,从Landsat TM遥感图像计算NDVI、SR、RSR、SAVI植被指数,分别建立估算LAI的单植被指数统计模型、多植被指数组合的改进BP神经网络,获取最有效描述LAI与植被指数非线性关系的方法并应用到TM图像估算北京城区LAI。结果表明,单植被指数非线性统计模型估算LAI的精度高于线性统计模型;多植被指数组合神经网络中,以NDVI、RSR、SAVI组合估算LAI的精度最高,估算值与观测值线性回归方程的R2最高,为0.827,而RMSE最低,为0.189,神经网络解决了多植被指数组合统计模型非线性回归方程的系数较多、较难确定的问题,可较为有效的应用于遥感图像林地LAI的估算。     

1988-2013年基于BP神经网络的植被叶面积指数遥感定量反演

叶面积指数（Leaf Area Index,LAI）高度综合了植被水平覆盖状况和垂直结构,以及枯枝落叶层厚薄和地下生物量多少,是植被影响土壤侵蚀的主要方面。区域尺度的时间序列叶面积指数揭示了区域土壤侵蚀的演化过程。因此,及时准确地掌握区域尺度上长时间序列的植被LAI,对研究土壤侵蚀动态变化与植被的关系至关重要。选择南京市1988-2013年10期遥感影像,基于反向传播（Back Propagation,BP）神经网络构建LAI反演模型,进行了长时间序列的叶面积指数反演。结合2009和2010年LAI实测值,验证与探讨了该模型的评价精度与适应性。结果表明：（1）该模型拟合度较高,2009和2010年平均相对误差、均方根误差、相关系数分别是0.2395和0.2174,0.2962和0.2581,0.7713和0.6844,各项精度评价指标均较好;（2）统计分析去除耕地后全市LAI变化,低植被覆盖（LAI<2）面积不断增加,高植被覆盖区（LAI>3）面积先减少后增加,耕地面积不断减少,符合南京市的发展变化规律;（3）主城区LAI年际变化与其他学者得到的南京市植被盖度变化趋势一致,反演结果的时序性较高。本文提出的基于反向传播神经网络模型反演长时间序列LAI是可行的,为区域尺度土壤侵蚀定量遥感监测提供新途径。     

全景绿视率自动识别和计算研究

绿视率是用于绿色空间感知的直观评价标准,传统研究的绿视率多基于平面影像进行计算,不能完全反映三维空间中人对绿量的主观感受。基于全景影像,提出全景绿视率的概念,通过全景相机获取球面全景照片,将等距圆柱投影转换为等积圆柱投影,利用基于语义分割的卷积神经网络模型,自动识别植被区域面积以实现全景绿视率自动化识别和计量。通过比较5项卷积神经网络模型对绿视率的识别效果,显示出Dilated ResNet-105神经网络模型具有最高的识别准确度。以武汉市武昌区紫阳公园为例,对各级园路和广场的全景绿视率进行计算和分析。将卷积神经网络的识别结果同人工判别结果进行对比研究,结果显示：使用Dilated ResNet-105卷积神经网络对绿植范围识别的平均交并比（mIoU）为62.53%,与人工识别的平均差异为9.17%。全景绿视率自动识别和计算可以为相关研究提供新的思路,实现客观准确、快速便捷的绿视率测量评估。     

Developing pedotransfer functions to estimate the S-index for indicating soil quality

Indicating soil quality usually requires many soil properties of which the measurements are time consuming. Therefore, it is desirable to developing simple and effective indices for reflecting soil quality based on soil properties that can be readily obtained. The soil physical quality index, S-index, derived from the slope at the inflection point of the water retention curve (particularly the Van-Genuchten equation), is a comprehensive index for indicating soil properties. By comparing the S-index with a widely used soil quality index (SQI), this study used 298 samples to determine soil chemical and physical properties for calculating SQI, and found that the correlation coefficient between the S-index and SQI was 0.88, indicating that the S-index can represent soil quality well. An artificial neural network (ANN) model and a linear regression (LR) model were proposed for estimating S-index. Results showed that the ANN model was better than LR model in estimating S-index. Particularly, the ANN model with the soil bulk density and soil organic carbon (scenario A1) as inputs, had the highest R² of 0.807, while the LR model get the highest R² (predicted v.s. observed) of 0.75 with the combination of soil organic carbon, soil bulk density, total nitrogen and available nitrogen. This study is helpful for extending the applications of S-index.     

A template matching model for pattern recognition: Self-organization of templates and template matching by a disinhibitory neural network

A template matching model for pattern recognition is proposed. By following a previouslyproposed algorithm for synaptic modification (Hirai, 1980), the template of a stimulus pattern is selforganized as a spatial distribution pattern of matured synapses on the cells receiving modifiable synapses. Template matching is performed by the disinhibitory neural network cascaded beyond the neural layer composed of the cells receiving the modifiable synapses. The performance of the model has been simulated on a digital computer. After repetitive presentations of a stimulus pattern, a cell receiving the modifiable synapses comes to have the template of that pattern. And the cell in the latter layer of the disinhibitory bitory neural network that receives the disinhibitory input from that cell becomes electively sensitive to that pattern. Learning patterns are not restricted by previously learned ones. They can be subset or superset patterns of the ones previously learned. If an unknown pattern is presented to the model, no cell beyond the disinhibitory neural network will respond. However, if previously learned patterns are embedded in that pattern, the cells which have the templates of those patterns respond and are assumed to transmit the information to higher center. The computer simulation also shows that the model can organize a clean template under a noisy environment.     

On looking for neural networks and “cell assemblies” that underlie behavior: II. Neural realization of the mathematical model

Part I (P. H. Greene,Bull. Math. Biophysics,24, 247–275, 1962) discussed a number of formal properties of animal behavior, and presented evidence that these properties would follow naturally from a model in which patterns of neural activity in perception or motor action constituted the resonant responses of linear neural networks. Equations were derived for parameters characterizing networks which would possess desired resonant responses. These equations expressed purely mathematical requirements. The present paper shows that a simple neural model would be entirely adequate to meet these requirements. According to this model, an input locus may become functionally connected to a particular resonant response mode by firing at a frequency which comes to approach the resonant frequency of that mode. The information in a complicated “cell assembly” of the type considered could be transmitted through a nerve tract by a very simple frequency code. One neurological guess is that frequency-coded inputs excite the transients in dendritic networks. If the amplitude of the pattern becomes large, as it would near resonance, the all-or-none axonal response would become excited. This axonal response would tend to augment resonant patterns and disrupt other patterns, for a reason inherent in any linear network. Since resonant responses are automatically present in any linear network, unless special processes suppress them, they must have led to overt behavior in animals first possessing such networks. Evolution either suppressed this feature or exploited it. Since its properties resemble those of animal behavior, the latter might be suspected. Some implications are presented regarding what a physiologist might have to look for when he studies a neural system. This research was supported by the Office of Naval Research under Contract No. Nonr 2121(17) NR 049-148. Reproduction in whole or in part is permitted for any purpose of the United States Government.     

Re-Evaluation of the AASHTO-Flexible Pavement Design Equation with Neural Network Modeling

Here we establish that equivalent single-axle loads values can be estimated using artificial neural networks without the complex design equality of American Association of State Highway and Transportation Officials (AASHTO). More importantly, we find that the neural network model gives the coefficients to be able to obtain the actual load values using the AASHTO design values. Thus, those design traffic values that might result in deterioration can be better calculated using the neural networks model than with the AASHTO design equation. The artificial neural network method is used for this purpose. The existing AASHTO flexible pavement design equation does not currently predict the pavement performance of the strategic highway research program (Long Term Pavement Performance studies) test sections very accurately, and typically over-estimates the number of equivalent single axle loads needed to cause a measured loss of the present serviceability index. Here we aimed to demonstrate that the proposed neural network model can more accurately represent the loads values data, compared against the performance of the AASHTO formula. It is concluded that the neural network may be an appropriate tool for the development of databased-nonparametric models of pavement performance.     

Optimizationof neural network architecture using genetic programming improvesdetection and modeling of gene-gene interactions in studies of humandiseases

Background

Appropriate definitionof neural network architecture prior to data analysis is crucialfor successful data mining. This can be challenging when the underlyingmodel of the data is unknown. The goal of this study was to determinewhether optimizing neural network architecture using genetic programmingas a machine learning strategy would improve the ability of neural networksto model and detect nonlinear interactions among genes in studiesof common human diseases.

Results

Using simulateddata, we show that a genetic programming optimized neural network approachis able to model gene-gene interactions as well as a traditionalback propagation neural network. Furthermore, the genetic programmingoptimized neural network is better than the traditional back propagationneural network approach in terms of predictive ability and powerto detect gene-gene interactions when non-functional polymorphismsare present.

Conclusion

This study suggeststhat a machine learning strategy for optimizing neural network architecturemay be preferable to traditional trial-and-error approaches forthe identification and characterization of gene-gene interactionsin common, complex human diseases.

     

Genetic interference reduces the evolvability of modular and non-modular visual neural networks

The aim of this paper is to propose an interdisciplinary evolutionary connectionism approach for the study of the evolution of modularity. It is argued that neural networks as a model of the nervous system and genetic algorithms as simulative models of biological evolution would allow us to formulate a clear and operative definition of module and to simulate the different evolutionary scenarios proposed for the origin of modularity. I will present a recent model in which the evolution of primate cortical visual streams is possible starting from non-modular neural networks. Simulation results not only confirm the existence of the phenomenon of neural interference in non-modular network architectures but also, for the first time, reveal the existence of another kind of interference at the genetic level, i.e. genetic interference, a new population genetic mechanism that is independent from the network architecture. Our simulations clearly show that genetic interference reduces the evolvability of visual neural networks and sexual reproduction can at least partially solve the problem of genetic interference. Finally, it is shown that entrusting the task of finding the neural network architecture to evolution and that of finding the network connection weights to learning is a way to completely avoid the problem of genetic interference. On the basis of this evidence, it is possible to formulate a new hypothesis on the origin of structural modularity, and thus to overcome the traditional dichotomy between innatist and empiricist theories of mind.     

Effective diffusion rate through a polymer network: influence of nonspecific binding and intersegment transfer

The effective diffusion rate of a tracer molecule through a polymer network can be influenced by nonspecific binding. If such binding occurs, the local density fluctuations (segmental diffusion) of the network molecules will contribute to the net displacements of tracer molecules. If the network is strongly interconnected by entanglement or cross-linking, these local motions will only carry the tracer molecules over a small region, and effective transport would require dissociation and reassociation of the tracer molecule to another part of the network. Alternatively, tracer molecules could be transferred directly (intersegment transfer) between different parts of the network whenever they are brought sufficiently close by the density fluctuations. A wormlike-chain model for the segmental diffusion of a polymer is used to describe the network motions and to derive the effective diffusion rate for a tracer molecule as a function of network density and binding constant with or without intersegment transfer contributing. It is found that the density dependence for the effective diffusion of ethidium bromide through dense DNA solutions studied by photobleaching recovery [R. D. Icenogle and E. L. Elson (1983) Biopolymers 22 , 1949–1966] agrees with an intersegment-transfer mechanism limited by the segmental DNA motions. The calculations are also applied to a model for the intracellular diffusion of molecules loosely bound to the cytomatrix. If intersegment transfer dominates it can account for the observed size independence for the intracellular diffusion rates of various injected macromolecules.     

Wavelet neural networks applied to pulping of oil palm fronds

In the organosolv pulping of the oil palm fronds, the influence of the operational variables of the pulping reactor (viz. cooking temperature and time, ethanol and NaOH concentration) on the properties of the resulting pulp (yield and kappa number) and paper sheets (tensile index and tear index) was investigated using a wavelet neural network model. The experimental results with error less than 0.0965 (in terms of MSE) were produced, and were then compared with those obtained from the response surface methodology. Performance assessment indicated that the neural network model possessed superior predictive ability than the polynomial model, since a very close agreement between the experimental and the predicted values was obtained.     

Pattern recall in networks of chaotic neurons

This research investigates the potential utility of chaotic dynamics in neural information processing. A novel chaotic spiking neural network model is presented which is composed of non-linear dynamic state (NDS) neurons. The activity of each NDS neuron is driven by a set of non-linear equations coupled with a threshold based spike output mechanism. If time-delayed self-connections are enabled then the network stabilises to a periodic pattern of activation. Previous publications of this work have demonstrated that the chaotic dynamics which drive the network activity ensure that an extremely large number of such periodic patterns can be generated by this network. This paper presents a major extension to this model which enables the network to recall a pattern of activity from a selection of previously stabilised patterns.     

桤柏混交林密度变化规律的人工神经网络模型研究

本文应用人工神经网络方法建立了桤柏混交林密度变化的神经网络模型,并与传统模型进行了比较,仿真结果表明,人工神经网络模型可适用于桤柏混交林密度变化规律描述,且优于传统模型,从而丰富和发展了森林稀疏规律理论。     

Higherorder neural network group models for financial simulation

Real world financial data is often discontinuous and non-smooth. If we attempt to use neural networks to simulate such functions, then accuracy will be a problem. Neural network group models perform this function much better. Both Polynomial Higher Order Neural network Group (PHONG) and Trigonometric polynomial Higher Order Neural network Group (THONG) models are developed. These HONG models are open box, convergent models capable of approximating any kind of piecewise continuous function, to any degree of accuracy. Moreover they are capable of handling higher frequency, higher order non-linear and discontinuous data. Results obtained using a Higher Order Neural network Group financial simulator are presented, which confirm that HONG group models converge without difficulty, and are considerably more accurate than neural network models (more specifically, around twice as good for prediction, and a factor of four improvement in the case of simulation).     

Paternity index and attribution of paternity

If blood typing and similar tests do not exclude a putative father in a paternity case, his probability of paternity can be assessed with the formulae of Essen-M?ller[1938]. Gürtler[1956] uses an alternative route, viz. the paternity index, to reach identical end results. Majumder and Nei [1983] claim that the methods are not powerful enough. This opinion can always be defended, but may have been enhanced by their inadequate computer model. They also contend that current methods may more often than not lead to false attributions of paternity. This is outright erroneous.     

Bioindication in Norwegian rivers using non-diatomaceous benthic algae: The acidification index periphyton (AIP)

Impacts of acidification on aquatic communities have been obvious in Scandinavia since the end of the 19th century and recent model simulations show that in Norway, acidification of surface waters will continue to be an issue in the coming decades. Here, we present a new index based on non-diatomaceous benthic algae (acidification index periphyton, AIP) that can be used to describe the mean annual acidity of Norwegian rivers. The AIP was applied to 608 samples from unlimed rivers all over Norway and values ranged from 5.35 to 7.28, thus covering a range from acid to neutral conditions. Application of the AIP to both limed sites and sites that formerly were acidified demonstrate that the algal community reacts with a several years delay to both river liming and natural recovery. The AIP is most sensitive between mean annual pH values of approximately 5.5 and 7.0 and can be especially useful in detecting the first signs of an acidification trend or the last steps of a recovery process.