人工神经网络模型在水稻群体分蘖动态模拟中外推能力的测试

研究利用人工神经网络模型 ,以水稻群体分蘖动态为例 ,采用交互验证和独立验证的方式 ,对水稻生长 BP网络模型进行了训练与模拟 ,其结果与水稻群体分蘖的积温统计模型、基本动力学模型和复合分蘖模型进行了比较。研究结果表明 ,神经网络模型具有一定的外推能力 ,但其外推能力依赖于大量的训练样本。神经网络模型具有较好的拟合能力 ,是因为有较多的模型参数 ,因此对神经网络模型的训练需要大量的参数来保证其参数不致过度吻合。具有外推能力神经网络模型的最少训练样本数应大于 6 .75倍于神经网络参数数目 ,小于 13.5倍于神经网络参数数目。因此在应用神经网络模型时 ,如果神经网络模型包括较多的输入变量时 ,可考虑采用主成分分析、对应分析等技术对输入变量进行信息综合 ,相应地减少网络模型的参数。另一方面 ,当训练样本不足时 ,最好只用神经网络模型对同一系统的情况进行模拟 ,应谨慎使用神经网络模型进行外推。神经网络模型给作物模拟研究的科学工作者提供了一个“傻瓜”式工具 ,对数学建模不熟悉的农业研究人员 ,人工神经网络可以替代数学建模进行仿真实验 ;对于精通数学建模的研究人员来说 ,它至少是一种补充和可作为比较的非线性数据处理方法     

农业废弃物养分循环利用技术模式评估模型的研究进展

农业废弃物的养分循环利用技术模式是实现农业循环经济的重要手段,其评估模型为优化养分循环利用技术提供了重要支撑。本文总结了农业废弃物养分循环技术模式评估框架、评估模型及评价指标、模型的数据源及其不确定性分析,以及模型应用尺度的研究进展。当前,常用于评估养分流动的模型主要是过程数学模型和产业生态学模型。过程数学模型和产业生态学模型在评估结果的可靠性和模拟尺度上存在较大差异,前者主要集中在实验室或中试规模,精度较高;后者可以实现从微观到宏观的多尺度模拟,数据的获取方式导致其具有较高的不确定性。最后,本文对农业废弃物养分循环利用技术评估模型的研究进行展望,提出为了在区域尺度上实现对农业生产系统废弃物资源化利用技术的准确评估,可以将过程数学模型与工业生态学模型相结合,建立可靠的模型框架和数据库,同时,在工厂、农场、村落、乡镇、区域等地理尺度进行模型拓展研究。     

单木生物量模型估计区域尺度生物量的不确定性

采用系统抽样体系江西省固定样地杉木连续观测数据和生物量数据,通过Monte Carlo法反复模拟由单木生物量模型推算区域尺度地上生物量的过程,估计了江西省杉木地上总生物量。基于不同水平建模样本量n及不同决定系数R~2的设计,分别研究了单木生物量模型参数变异性及模型残差变异性对区域尺度生物量估计不确定性的影响。研究结果表明:2009年江西省杉木地上生物量估计值为(19.84±1.27)t/hm~2,不确定性占生物量估计值约6.41%。生物量估计值和不确定性值达到平稳状态所需的运算时间随建模样本量及决定系数R~2的增大而减小;相对于模型参数变异性,残差变异性对不确定性的影响更小。     

农业面源污染管理政策调控仿真模型研究进展

农业面源污染因其分散性、滞后性和不确定性而成为环境治理的难点。面源污染物迁移转化过程的模拟模型发展迅速,而农业面源污染管理政策仿真评估模型研究相对较为滞后。系统梳理与分析了自上而下宏观目标约束、自下而上微观行为传导及宏观与微观上下耦合三类政策仿真模型研究进展,总结了当前模型的基础理论与方法的优势与不足。本文提出了融合流域水系统过程规律认知,构建宏观政策目标自上而下约束与微观主体行为自下而上传导耦合的农业面源污染政策仿真模型,实现政策仿真模拟在国家、区域、流域与栅格跨尺度上的嵌套传导、参数互验与系统预测,以解决因农业面源污染防治主体的多元性与防治对象的广泛性特征所导致的复杂性系统难题,使得政策效应模拟仿真结果空间显性化,进而实现农业面源污染控制的政策管理由粗放型向精准化方向迈进。     

人工神经网络与遗传算法相结合在作物估产中的应用——以吉林省玉米估产为例

在遗传算法(Genetic Algorithm)与误差反传(Back Propagation)网络结构模型相结合的基础上,设计了用遗传算法训练神经网络权重的新方法,并对吉林省梨树和德惠县的玉米进行了估产研究,同时与BP算法和灰色系统理论模型进行了比较.经检验,计算值与实际值接近,并优于灰色理论模型,具有良好的预测效果,从而为农作物估产提供了新方法.     

人工神经网络与遗传算法相结合在作物估产中的应用——以吉林省米估产为例

在遗传算法（Genetic Algorithm）与误差反传（Back Propagation）网络结构模型相结合的基础上,设计了用遗传算法训练神经网络权重的新方法,并对吉林省梨树和德惠县的玉米进行了估产研究,同时与BP算法和灰色系统理论模型进行了比较。经经验,计算值与实际值拉近,并优于灰色理论模型,具有良好的预测效果,从而为农作物估产提供了新方法。     

条件联想神经网络研究

在人脑的某些功能和神经系统中的突前抑制机制启发下,本文提出一个新型的神经网络模型——条件联想神经网络.模型是一个有突触前抑制的联想记忆神经网络.通过初步分析和计算机模拟,证明本模型具有一般联想记忆模型所未有的一些新的特性,如可以在不同条件下,对同一输入有不同的反应.对同一输入,在不同的条件下,又可以有相同的反应.这些特点将有助于人们对神经系统中信息处理过程的了解.此外,文中也指出可能实现本模型的神经结构.     

基于生态信息诊断框架的杉木人工林生物量生态预测研究

森林生物量会直接影响森林生态系统服务的评估。如何运用景感生态学,准确预测区域尺度下森林生物量的时空演变趋势,是关乎国家重大方针政策制定和生态产业体系建设的关键性战略课题。本研究目的是构建一套生态信息诊断框架,优化趋善化模型（3PG2模型）结构,解决由于模型结构设计所导致在森林景感营造过程中生态预测的不确定性。以杉木林分布广泛的福建南靖县为研究区域,选择合适的阈值范围和空间统计分析识别出模拟生物量的不确定性区域,构建包含Geogdetector软件、遗传技术和计算机程序3个部分组成的生态信息诊断框架,使用Geogdetector软件阐明多重因素交互作用对模型模拟的影响及机理,采用遗传技术优化模型结构以提升模拟精度,运用计算机程序和3PG2模型准确预测区域尺度杉木林生物量的时空演变趋势。结果表明：林龄是导致3PG2模型生物量模拟结果不确定性的主导因素。通过景感生态学（谜码数据和趋善化模型）构建的生态信息诊断框架可以准确预测森林生物量,实现区域尺度上的可持续森林管理。     

运动过程的网络逻辑——从离子通道到动物行为

为了揭示神经网络在脊椎动物运动中所行使的内在功能,作者开发了七鳃鳗这种低等脊椎动物模型。在这套系统中,不仅可以了解到运动模式生成网络以及激活此网络的命令系统,同时还可以在运动中研究方向控制系统和变向控制系统。七鳃鳗的神经系统有较少的神经元,而且运动行为中的不同运动模式可以由分离的神经系统所引发。模式生成神经网络包括同侧的谷氨酸能中间神经元和对侧的抑制性甘氨酸能中间神经元。网络中的突触连接、细胞膜特性和神经递质都也已经被鉴定。运动是由脑干区域的网状脊髓神经元所引起,而这些神经元又是被问脑和中脑分离的一些运动命令神经元群所控制。因此,运动行为最初是由这两个“运动核心”所启动。而这两个运动核心被基底神经节调控,基底神经节即时地做出判断是否允许下游的运动程序启动。在静止情况下基底神经节的输出核团维持对下游不同运动核心的抑制作用,反之则去除抑制活化运动核心。纹状体和苍白球被认为是这个运动抉择系统的主要部件。根据“霍奇金一贺胥黎”模型神经元开发了这套网络模型,不同的细胞具有各自相应的不同亚型的钠、钾、钙离子通道和钙依赖的钾通道。每个模型神经元拥有86个不同区域模块以及其对应的生物学功能,例如频率控制、超极化等等。然后根据已有实验证据,利用突触将不同的模型神经元相连。而系统中的10000个神经元大致和生物学网络上的细胞数量相当。突触数量为760000。突触类型有AMPA、NMDA、glycine型。有了这样大规模的模型,不仅可以模拟肌节与肌节之间的神经网络,还可以模拟到由基底神经节开始的行为起始部分。此外,这些网络模拟还被用于一个神经机械学模型来模拟包含有推进和方向控制部分的真实运动。     

三种森林生物量估测模型的比较分析

森林生物量的定量估算为全球碳储量、碳循环研究提供了重要的参考依据。该研究采用黑龙江长白山地区的TM影像和133块森林资源一类清查样地的数据, 选取地学参数、遥感反演参数等71个自变量分别构建多元逐步回归模型、传统BP (back propagation)神经网络模型和基于高斯误差函数的BP神经网络改进模型(Gaussian error function, Erf-BP), 进而估算该地区的森林生物量, 并进行比较分析。结果表明, 多元逐步回归模型估测的森林生物量预测精度为75%, 均方根误差为26.87 t·m^-2; 传统BP神经网络模型估测森林生物量的预测精度为80.92%, 均方根误差为21.44 t·m^-2; Erf-BP估测森林生物量的预测精度为82.22%, 均方根误差为20.83 t·m^-2。可见, 改进后的Erf-BP能更好地模拟生物量与各个因子之间的关系, 估算精度更高。     

半干旱区春小麦生长系统的人工神经网络模型与产量预测

以半干旱区春小麦生长系统为研究对象。探讨了作物生长系统中水分、土壤养分等生态因子的时空变化特征及春小麦产量形成机制,应用人工神经网络方法建立了半干旱区春小麦生长系统的产量随环境因子变化的神经网络模型,并与传统的CTM模型进行了比较。模拟结果表明,人工神经网络模型可适用于半干旱区春小麦生长系统产量随环境因子变化规律描述,且优于传统模型,从而为春小麦产量预测提供了新的途径,也为作物生态系统的人工调控提供了新的模式与定量依据。     

The backpropagation algorithm in J, a fast prototyping tool for researching neural networks

This paper illustrates the use of a powerful language, called J, that is ideal for simulating neural networks. The use of J is demonstrated by its application to a gradient descent method for training a multilayer perceptron. It is also shown how the back-propagation algorithm can be easily generalized to multilayer networks without any increase in complexity and that the algorithm can be completely expressed in an array notation which is directly executable through J. J is a general purpose language, which means that its user is given a flexibility not available in neural network simulators or in software packages such as MATLAB. Yet, because of its numerous operators, J allows a very succinct code to be used, leading to a tremendous decrease in development time.     

EEG source localization: comparative study of classical and neural network methods.

We address the problem of estimating biopotential sources within the brain, based on EEG signals observed on the scalp. This problem, known as the inverse problem of electrophysiology, has no closed-form solution, and requires iterative techniques such as the Levenberg-Marquardt (LM) algorithm. Considering the nonlinear nature of the inverse problem, and the low signal to noise ratio inherent in EEG signals, a backpropagation neural network (BPN) has been recently proposed as a solution. The technique has not been properly compared with classical techniques such as the LM method, or with more recent neural network techniques such as the Radial Basis Function (RBF) network. In this paper, we provide improved strategies based on BPN and consider RBF networks in solving the inverse problem. We compare the performances of BPN, RBF and a hybrid technique with that of the classical LM method.     

Artificial intelligence-guided analysis of cytologic data

A design for the integration of artificial intelligence (AI) technology with large databases of clinical and objective cytologic data, such as are on file at the University of Chicago, is presented. Among the key features of this approach are the use of a knowledge representation structure based upon an associative network, the use of a Bayesian belief network as a method of managing uncertainty in the system, and the use of neural networks and unsupervised learning algorithms as a means of discovering patterns within this database. Such an automated approach is necessary, given the complexity and interdependence of these data, to gain an understanding of their dependence structure and to assist in their exploration and analysis.     

Pruning artificial neural networks using neural complexity measures

This paper describes a new method for pruning artificial neural networks, using a measure of the neural complexity of the neural network. This measure is used to determine the connections that should be pruned. The measure computes the information-theoretic complexity of a neural network, which is similar to, yet different from previous research on pruning. The method proposed here shows how overly large and complex networks can be reduced in size, whilst retaining learnt behaviour and fitness. The technique proposed here helps to discover a network topology that matches the complexity of the problem it is meant to solve. This novel pruning technique is tested in a robot control domain, simulating a racecar. It is shown, that the proposed pruning method is a significant improvement over the most commonly used pruning method Magnitude Based Pruning. Furthermore, some of the pruned networks prove to be faster learners than the benchmark network that they originate from. This means that this pruning method can also help to unleash hidden potential in a network, because the learning time decreases substantially for a pruned a network, due to the reduction of dimensionality of the network.     

基于TM影像、森林资源清查数据和人工神经网络的森林碳空间分布模拟

森林是陆地生态系统中最大的碳库,在全球碳平衡和减缓全球气候变化方面发挥着不可替代的作用。当前主要利用森林资源清查数据和优势树种材积源-生物量的关系进行碳储量估算,在此基础上有效结合遥感影像数据将会更好的满足相关部门对国家和区域森林碳储量计算的需求。利用临安市2004年森林资源清查的930个样地数据和同年度Landsat TM影像数据,提取6个波段灰度值以及与碳储量相关性相对较大的3个波段组合,结合人工神经网络对研究区森林碳储量及其分布进行有效模拟。结果显示,用误差反向传播算法训练神经网络较好的重建了森林碳密度空间分布和变化,森林碳地上部分模拟结果与样地实测值之间的一致性好,全区域模拟结果森林碳平均值为0.98Mg(10.89Mg/hm²),总体森林碳密度模拟结果低于样地平均值约13%,进一步验证了人工神经网络在对大范围森林碳估算与模拟上具有较好的效果,为区域森林碳储量的估测研究提供有效的方法支持。     

Neural model of the genetic network

Many cell control processes consist of networks of interacting elements that affect the state of each other over time. Such an arrangement resembles the principles of artificial neural networks, in which the state of a particular node depends on the combination of the states of other neurons. The lambda bacteriophage lysis/lysogeny decision circuit can be represented by such a network. It is used here as a model for testing the validity of a neural approach to the analysis of genetic networks. The model considers multigenic regulation including positive and negative feedback. It is used to simulate the dynamics of the lambda phage regulatory system; the results are compared with experimental observation. The comparison proves that the neural network model describes behavior of the system in full agreement with experiments; moreover, it predicts its function in experimentally inaccessible situations and explains the experimental observations. The application of the principles of neural networks to the cell control system leads to conclusions about the stability and redundancy of genetic networks and the cell functionality. Reverse engineering of the biochemical pathways from proteomics and DNA micro array data using the suggested neural network model is discussed.     

Adaptive Neural Network Structure Optimization Algorithm Based on Dynamic Nodes

Large-scale artificial neural networks have many redundant structures, making the network fall into the issue of local optimization and extended training time. Moreover, existing neural network topology optimization algorithms have the disadvantage of many calculations and complex network structure modeling. We propose a Dynamic Node-based neural network Structure optimization algorithm (DNS) to handle these issues. DNS consists of two steps: the generation step and the pruning step. In the generation step, the network generates hidden layers layer by layer until accuracy reaches the threshold. Then, the network uses a pruning algorithm based on Hebb’s rule or Pearson’s correlation for adaptation in the pruning step. In addition, we combine genetic algorithm to optimize DNS (GA-DNS). Experimental results show that compared with traditional neural network topology optimization algorithms, GA-DNS can generate neural networks with higher construction efficiency, lower structure complexity, and higher classification accuracy.     

Artificial astrocytes improve neural network performance

Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN) and artificial neuron-glia networks (NGN) to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function.