神经网络与人事决策研究的新范式

随着多特征决策研究的深入,传统方法已经不能回答更加细致的问题。细察精确预测的理论、建立模型与数据的形式关系成为更有希望的研究方向。神经网络模型设计用来模拟许多并行的认知和神经行为,具有样例学习和迁移适应能力．在解释和预测方面具有传统方法所不具备的潜力。神经网络能够同时表征线性补偿和非补偿规则,其应用已经渗透到许多学科领域。网络范式对于人事研究和应用也有价值,有研究表明神经网络可以用于人力资源管理的一般领域,成为人事决策研究的新范式。     

嗅觉系统神经网络模型的模拟与动力学特性分析

在哺乳动物嗅觉系统的拓扑结构及生理实验的基础上建立了一套非线性动力学神经网络模型．此模型在模拟嗅觉神经系统方面有着突出的优点,同时在信号处理以及模式识别中表现出了奇异的混沌特性．着重描述了K系列模型的非线性动力学特性,并通过数值模拟进行分析．     

神经网络中伴随着搜索过程的联想

1982年J.J.Hopfield提出了他的神经网络模型,并发现它具有许多集合运算特性。由于这一模型与凝聚态理论的自旋玻璃模型有惊人的相似,可在研究中引人物理学的诸如统计力学等科学方法来考察神经网络的特性,其中对网络稳态条件的研究提出了网络综合的观点,即怎样决定突触矩阵和阈值等     

一类具有漏泄时滞的混沌神经网络的反同步

利用矩阵测度方法和微分不等式技巧,研究了一类具有漏泄时滞的混沌神经网络的反同步问题.数值模拟验证了理论分析的正确性.     

Hindmarsh-Rose 神经网络的混沌同步

研究了通过特殊构造的非线性函数耦合连接的神经网络的混沌同步问题。在发展基于稳定性准则的混沌同步方法的基础上,给出了计算同步稳定性的误差发展方程,当耦合强度取参考值时,可实现稳定的混沌同步而不需要计算最大条件Lyapunov指数去判定是否稳定。通过对按照完全连接形式构成的Hindmarsh-Rose神经网络的数值模拟,显示可仅从两个耦合神经的耦合强度的稳定性范围预期到许多耦合神经实现同步的稳定性范围。该方法在噪声影响下,对实现神经元的混沌同步仍具有较强的鲁棒性。此外发现随着耦合神经数的增加,满足同步稳定性的耦合强度减小,与耦合神经的数量成反比。     

离散广义Logistic模型的混沌控制

利用Lyapunov指数方法,验证了一类离散广义Logistic模型存在混沌现象,并采用混沌控制中OGY方法的基本思想,研究了这类模型的混沌控制问题,得出了消除混沌,保持种群稳定到不动点和2-周期轨道的充分条件．     

马尾松自疏规律的人工神经网络模型研究

森林自然稀疏规律的研究已经有了很大发展,并提出了许多经验的或理论的表达式。本研究介绍了人工神经网络方法,首次建立了马尾松人工林自然稀疏规律的三层前馈反向传播神经网络模型。仿真结果表明,人工神经网络模型能很好地符合实际的观测资料,具有良好的使用价值,从而丰富了该领域的研究方法。     

病毒基因组启动子识别的人工神经网络方法

本文运用神经网络方法,并结合病毒基因分子生物学有关理论与统计事实,对病毒基因启动子区域进行了识别,文中选择了共35个基因组,作为研究对象．学习组选择了28个基因组,预测组选择了7个基因组,结果表明,将神经网络模型与病毒基因有关理论相结合,能够运用计算方法,以大量的可能启动子组合中排列出唯一的启动子区域．     

细菌在土壤中运移模型的研究进展

大多数细菌运移实验是在室内控制条件下土柱内进行,得到的结果却不尽一致,有必要对该方法进行标准化;此外,可采用与显微镜结合研究孔隙尺度下的细菌运移．本文认为依据对流-弥散方程建立的细菌运移的数学模型,在一定限制条件下,与实验结果拟合较好,但模型忽略了许多参数,如生长、死亡．若在大田尺度下应用,还应对机理理论、实验范围、尺度转换、模型建立进行更深入的研究．     

一类时滞神经网络模型的稳定性与全局Hopf分支

研究了一类由两个神经元构成的时滞神经网络模型的稳定性和局部Hopf分支,并结合一般泛函微分方程的全局Hopf分支定理,利用度理论研究了全局Hopf分支的存在性．     

害虫灾害研究的复杂性理论框架

害虫灾害是高度复杂的大系统 ,表现出不均匀性、差异性、多样性、突发性、随机性、可预测性和周期性等复杂性特征 ,使得经典的理论和方法已不适用于害虫灾害的研究。依据复杂性科学和分形、神经网络、混沌及小波等非线性科学的发展及其近期在害虫灾害中的部分研究成果 ,该文从复杂大系统出发 ,构建了害虫灾害研究的复杂性理论框架 ,为深入研究害虫灾害的成因、机制与预测提供理论依据。     

土地生态系统的复杂性研究

运用复杂科学理论,阐述了土地生态系统的复杂性特征,包括多层次性,高维性,子系统关联的复杂性,结构与功能的不确定性,开放性,动态性,自适应性和自组织性等复杂性特征,并进一步探讨了分形,混沌及人工神经网络在土地生态系统复杂性特征研究中的应用。     

Modelling and prediction for chaotic fir laser attractor using rational function neural network

Many real-world systems such as irregular ECG signal, volatility of currency exchange rate and heated fluid reaction exhibit highly complex nonlinear characteristic known as chaos. These chaotic systems cannot be retreated satisfactorily using linear system theory due to its high dimensionality and irregularity. This research focuses on prediction and modelling of chaotic FIR (Far InfraRed) laser system for which the underlying equations are not given. This paper proposed a method for prediction and modelling a chaotic FIR laser time series using rational function neural network. Three network architectures, TDNN (Time Delayed Neural Network), RBF (radial basis function) network and the RF (rational function) network, are also presented. Comparisons between these networks performance show the improvements introduced by the RF network in terms of a decrement in network complexity and better ability of predictability.     

Interactions between neural networks: a mechanism for tuning chaos and oscillations

We show that chaos and oscillations in a higher-order binary neural network can be tuned effectively using interactions between neural networks. Our results suggest that network interactions may be useful as a means of adjusting the level of dynamic activities in systems that employ chaos and oscillations for information processing, or as a means of suppressing oscillatory behaviors in systems that require stability. URL: http:// www.ntu.edu.sg/home/elpwang     

Quantitative analysis of electroencephalograms: is there chaos in the future?

The history of quantitative, computerized electroencephalogram (EEG) analysis is reviewed. It is shown that, until very recently, the basic approach to EEG analysis involved the assumption that the EEG is stochastic. Consequently, statistical pattern recognition techniques, segmentation procedures, syntactic methods, knowledge-based approaches, and even artificial neural network methods have been developed with different levels of success. A fundamentally different approach to computerized EEG analysis, however, is making its way into the laboratories. The basic idea, inspired by recent advances in the area of non-linear dynamics, and especially the theory of chaos, is to view an EEG as the output of a deterministic system of relatively simple complexity, but containing non-linearities. This suggests that studying the geometrical dynamics of EEGs, and the development of neurophysiologically realistic models of EEG generation may produce more successful automated EEG analysis techniques than the classical, stochastic methods. Evidence supporting the non-linear dynamics paradigm is reviewed, and possible research paths are indicated.     

Cytoskeletal involvement in neuronal learning: a review

This paper introduces the ideas of neural networks in the context of currently recognized cellular structures within neurons. Neural network models and paradigms require adaptation of synapses for learning to occur in the network. Some models of learning paradigms require information to move from axon to dendrite. This motivated us to examine the possibility of intracellular signaling to mediate such signals. The cytoskeleton forms a substrate for intracellular signaling via material transport and other putative mechanisms. Furthermore, many experimental results suggest a link between the cytoskeleton and cognitive processing. In this paper we review research on intracellular signaling in the context of neural network learning.Abbreviations MT microtubule - MTs microtubules - ART adaptive resonance theory - RCE restricted coulomb energy - MAP microtubule associated protein - NO nitric oxide Correspondence to: J. Dayhoff     

软计算在生态模型中的应用

由于生态系统的高度复杂性和非线性以及空间数据采集技术的快速发展,近年来越来越多的软计算方法开始应用到生态模拟中来。软计算是个非常广泛的领域,在模式上主要包括元胞自动机、基于个体和盒式模式等;在方法上代表性的有人工神经网络、模糊数学、遗传算法、混沌理论等。重点介绍元胞自动机和规律方法在生态模型中的应用,具体实例包括种群动态模拟、水华预警和生境栖息地模拟。     

变时滞区间细胞神经网络的全局鲁棒稳定性

利用拓扑度理论和Liapunov泛函方法讨论了变时滞区间细胞神经网络的全局鲁棒稳定性．给出了实用有效的判定条件,推广了有关文献中的结果．     

The immune system as a neural network: a multi-epitope approach.

The term "neural network" has been applied to arrays of simple activation units linked by weighted connections. If the connections are modified according to a defined learning algorithm, such networks can be trained to store and retrieve patterned information. Memories are distributed throughout the network, allowing the network to recall complete patterns from incomplete input (pattern completion). The major biological application of neural network theory to date has been in the neurosciences, but the immune system may represent an alternative organ system in which to search for neural network architecture. Previous applications of parallel distributed processing to idiotype network theory have focused upon the recognition of individual epitopes. We argue here that this approach may be too restrictive, underestimating the power of neural network architecture. We propose that the network stores and retrieves large, complex patterns consisting of multiple epitopes separated in time and space. Such a network would be capable of perceiving an entire bacterium, and of storing the time course of a viral infection. While recognition of solitary epitopes occurs at the cellular level in this model, recognition of structures larger than the width of an antibody binding site takes place at the organ level, via network architecture integration of, i.e. individual epitope responses. The Oudin-Cazenave enigma, the sharing of idiotypic determinants by antibodies directed against distinct regions of the same antigen, suggests that some network level of integration of the individual clonal responses to large antigens does occur. The role of cytokines in prior neural network models of the immune system is unclear. We speculate that cytokines may influence the temperature of the network, such that changes in the cytokine milieu serve to "anneal" the network, allowing it to achieve the optimum steady-state in the shortest period of time.     

Splice sites detection using chaos game representation and neural network

A novel method is proposed to detect the acceptor and donor splice sites using chaos game representation and artificial neural network. In order to achieve high accuracy, inputs to the neural network, or feature vector, shall reflect the true nature of the DNA segments. Therefore it is important to have one-to-one numerical representation, i.e. a feature vector should be able to represent the original data. Chaos game representation (CGR) is an iterative mapping technique that assigns each nucleotide in a DNA sequence to a respective position on the plane in a one-to-one manner. Using CGR, a DNA sequence can be mapped to a numerical sequence that reflects the true nature of the original sequence. In this research, we propose to use CGR as feature input to a neural network to detect splice sites on the NN269 dataset. Computational experiments indicate that this approach gives good accuracy while being simpler than other methods in the literature, with only one neural network component. The code and data for our method can be accessed from this link: https://github.com/thoang3/portfolio/tree/SpliceSites_ANN_CGR.