BP神经网络及其在数据分类中的应用

BP神经网络模型是一种典型的前向型神经网络,具有良好的自学习、自适应、联想记忆、并行处理和非线形转换的能力,是目前应用最为广泛的一种神经网络模型。本文介绍了BP神经网络的实现以及其在数据挖掘分类方面的应用。     

基于复合神经网络的学习风格动态分析与研究

近年随着慕课（MOOC）等新兴教育教学手段的快速发展,大量的学习者学习行为可以被系统所记录和分析,从而为个性化教学奠定了重要基础。在Felder-Silverman学习风格模型的理论基础上,通过引入智能分析算法动态地分析和识别学习者学习风格,构建了一套融合了卷积神经网络和循环神经网络的“识别-推理”复合模型,通过学习者的线上学习行为、社区交互行为、学习内容浏览行为、点击拖动行为等学习过程识别其学习行为特征,并使用基于门控循环单元（Gated Recurrent Unit,GRU）的循环神经网络处理和预测其可能的学习风格及对学习内容形式的偏好,以更高效地为学习者提供适应于其学习风格的学习内容和路径,优化学习体验,为大规模、个性化和高质量的下一代学习平台提供技术支撑。     

回声状态网络及其在图像边缘检测中的应用

循环神经网络（RNN,也称反馈神经网络）是一种重要的人工神经网络,与前馈神经网络相比具有更好的学习能力和更快的收敛速度,但其隐层结构的设计一直是个难点问题。回声状态网络（ESN）有效地解决了上述问题,相比于以前的循环神经网络,其具有结构独特、稳定性好、学习过程简单快捷等特点。介绍了回声状态网络及其学习方法,将其用于图像的边缘检测中,取得了良好的效果。     

Novel Approaches to Probabilistic Neural Networks Through Bagging and Evolutionary Estimating of Prior Probabilities

In this contribution, novel approaches are proposed for the improvement of the performance of Probabilistic Neural Networks as well as the recently proposed Evolutionary Probabilistic Neural Networks. The Evolutionary Probabilistic Neural Network’s matrix of spread parameters is allowed to have different values in each class of neurons, resulting in a more flexible model that fits the data better and Particle Swarm Optimization is also employed for the estimation of the Probabilistic Neural Networks’s prior probabilities of each class. Moreover, the bagging technique is used to create an ensemble of Evolutionary Probabilistic Neural Networks in order to further improve the model’s performance. The above approaches have been applied to several well-known and widely used benchmark problems with promising results.      

深度神经网络的小样本学习综述

随着最近深度学习技术的蓬勃发展,深度神经网络（DNN）在大规模的图像分类与识别任务中取得了突破性的进展,但其在解决小样本学习问题时仍面临巨大挑战。小样本学习（FSL）是指在少量有监督样本的情况下学习一个能解决实际问题的模型,在深度学习领域具有重要意义。这促使该系统梳理了已有的DNN下的小样本学习工作,根据它们在解决小样本学习问题时所采用的技术,将DNN下的小样本学习解决方案分为四种策略：数据增强、度量学习、外部记忆、参数优化。根据这些策略,对现有的DNN下的小样本学习方法进行了全面的综述,同时总结了每一种策略在相关基准上的表现。强调了现有技术存在的局限性并对其未来的发展方向进行了展望,为今后的研究工作提供参考。     

改进的小波神经网络在桥梁损伤中的预测研究

提出基于BP算法的小波神经网络改进算法。仿真结果表明它避免了BP 神经网络结构设计的盲目性和局部最优等非线性优化问题,简化了训练,具有较强的函数学习能力和推广能力。该算法成功应用于桥梁损伤预测,具有广泛的应用前景。     

容噪学习机制及其在Robocup中的应用研究

噪声对机器学习影响极大，不庸忽视，因此提出一种有效的容噪机制是当前研究的热点问题。本文研究了BP神经网络在不同噪声环境下的容噪能力，利用容噪BP神经网络分析了机器人足球中的噪声问题及其解决方案，并将这种BP网络应用到了我们的仿真球队NDSocTeam的截球动作上，其效果良好。     

基于粗糙集理论的模糊神经网络构造方法

构造模糊神经网络时确定初始的隶属函数是一个难点,提出了一种新的基于粗糙集理论的隶属函数获取算法,该算法根据粗糙集理论中基于属性重要性的离散化方法确定条件属性的断点,再通过断点确定各模糊集合隶属函数的中心和宽度,同时给出了网络各参数的修正公式;仿真结果证明,该算法在学习的快速性和精度上具有良好的性能.     

A temporal model of linear anti-Hebbian learning

A temporal variant of Foldiak's first model with lateral inhibitory synaptic weights is proposed. The usual symmetric scalar values of the lateral weights are replaced with data driven asymmetric memory based lateral weights, which take the form of Finite Impulse Response (FIR) coefficients. Linear anti-Hebbian learning, as defined by Foldiak (IEEE/INNS International Joint Conference on Neural Networks, 1989) and Matsuoka et al. (Neural Networks, Vol. 8, pp. 411–419, 1995), is employed in the self-organisation of the network weights. The temporal anti-Hebbian learning, when applied to the separation of convolved mixtures of signals, causes the network weights to converge to the truncated FIR filter coefficients of the unmixing transfer function and so recover the original signals. Simulation results are presented for separating two natural speech sources convolved and mixed by a priori unknown direct and cross-coupled transfer functions. We compare temporal anti-Hebbian learning with information maximisation learning when applied to the blind separation of convolved sources.     

AVLR-EBP: A Variable Step Size Approach to Speed-up the Convergence of Error Back-Propagation Algorithm

A critical issue of Neural Network based large-scale data mining algorithms is how to speed up their learning algorithm. This problem is particularly challenging for Error Back-Propagation (EBP) algorithm in Multi-Layered Perceptron (MLP) Neural Networks due to their significant applications in many scientific and engineering problems. In this paper, we propose an Adaptive Variable Learning Rate EBP algorithm to attack the challenging problem of reducing the convergence time in an EBP algorithm, aiming to have a high-speed convergence in comparison with standard EBP algorithm. The idea is inspired from adaptive filtering, which leaded us into two semi-similar methods of calculating the learning rate. Mathematical analysis of AVLR-EBP algorithm confirms its convergence property. The AVLR-EBP algorithm is utilized for data classification applications. Simulation results on many well-known data sets shall demonstrate that this algorithm reaches to a considerable reduction in convergence time in comparison to the standard EBP algorithm. The proposed algorithm, in classifying the IRIS, Wine, Breast Cancer, Semeion and SPECT Heart datasets shows a reduction of the learning epochs relative to the standard EBP algorithm.     

Neural networks for relational learning: an experimental comparison

In the last decade, connectionist models have been proposed that can process structured information directly. These methods, which are based on the use of graphs for the representation of the data and the relationships within the data, are particularly suitable for handling relational learning tasks. In this paper, two recently proposed architectures of this kind, i.e. Graph Neural Networks (GNNs) and Relational Neural Networks (RelNNs), are compared and discussed, along with their corresponding learning schemes. The goal is to evaluate the performance of these methods on benchmarks that are commonly used by the relational learning community. Moreover, we also aim at reporting differences in the behavior of the two models, in order to gain insights on possible extensions of the approaches. Since RelNNs have been developed with the specific task of learning aggregate functions in mind, some experiments are run considering that particular task. In addition, we carry out more general experiments on the mutagenesis and the biodegradability datasets, on which several other relational learners have been evaluated. The experimental results are promising and suggest that RelNNs and GNNs can be a viable approach for learning on relational data.     

基于遗传算法的一种新的神经网络结构模式研究

神经网络与遗传算法的结合为解决大规模、复杂、并行的系统问题提供了广阔的前景，但其有着固有的缺陷和不足，本文在遗传算法的基础上，提出了一种新的神经网络结构构式，它能将遗传算法与神经网络更加有机地结合起来，改进其不足，增加神经网络的学习方式，提高学习效率和网络的稳定性。     

容限空间理论的扩展及其在人工神经网络中的应用

我们知道,在一般的代数空间中,集合X和Y的二元关系定义为XxY的子集.2~Inan在研究人的视觉结构中,从一般拓扑空间理论出发建立了容限空间理论。在容限空间理论中,定义了容限、容限空间等概念。这些概念的提出对于我们正确认识人工神经网络功能有着重要的指导意义。     

High-order neural network structure selection for function approximation applications using genetic algorithms

Neural network literature for function approximation is by now sufficiently rich. In its complete form, the problem entails both parametric (i.e., weights determination) and structural learning (i.e., structure selection). The majority of works deal with parametric uncertainty assuming knowledge of the appropriate neural structure. In this paper we present an algorithmic approach to determine the structure of High Order Neural Networks (HONNs), to solve function approximation problems. The method is based on a Genetic Algorithm (GA) and is equipped with a stable update law to guarantee parametric learning. Simulation results on an illustrative example highlight the performance and give some insight of the proposed approach.     

Ensemble of online sequential extreme learning machine

Liang et al. [A fast and accurate online sequential learning algorithm for feedforward networks, IEEE Transactions on Neural Networks 17 (6) (2006), 1411–1423] has proposed an online sequential learning algorithm called online sequential extreme learning machine (OS-ELM), which can learn the data one-by-one or chunk-by-chunk with fixed or varying chunk size. It has been shown [Liang et al., A fast and accurate online sequential learning algorithm for feedforward networks, IEEE Transactions on Neural Networks 17 (6) (2006) 1411–1423] that OS-ELM runs much faster and provides better generalization performance than other popular sequential learning algorithms. However, we find that the stability of OS-ELM can be further improved. In this paper, we propose an ensemble of online sequential extreme learning machine (EOS-ELM) based on OS-ELM. The results show that EOS-ELM is more stable and accurate than the original OS-ELM.     

机器学习算法在ALOS影像分类中的应用研究

首先介绍了CART、C5.0和概率神经网络三种机器学习算法的原理,然后以覆盖湖北省公安县的ALOS影像为数据源,从整体精度、对训练样本大小和噪声的敏感性三个方面对它们进行了比较分析。结果显示C5.0算法分类的整体精度最高,达到83.59%。概率神经网络受训练样本大小和噪声的影响最低:在训练样本大小降为原样本数据量的40%时,其精度为78.52%;噪声占训练样本量的10%时,精度只下降了4.3%。通过分析可以看出,在训练样本量充足时,C5.0算法的分类精度最好,而在样本不足或者包含噪声的情况下,使用概率神经网络算法能比其他两种算法取得更好的分类效果。     

基于CFNN的电火花加工工艺效果预测模型

针时常规神经网络和模糊神经网络的不足,介绍了一种具有快速算法的补偿模糊神经网络,并根据电火花加工的工艺特点及其复杂性,建立了基于补偿模糊神经网络的电火花加工工艺效果预测模型,可实现指定加工条件下的工艺效果预测.仿真结果显示了其良好的预测精度,其性能优于常规模糊神经网络.     

A Puzzle concerning Compositionality in Machines

Minds and Machines - This paper attempts to describe and address a specific puzzle related to compositionality in artificial networks such as Deep Neural Networks and machine learning in general....     

Deep Learning Guided Double Hidden Layer Neural Synchronization Through Mutual Learning

Neural Processing Letters - In this paper, a Double Hidden Layer Neural Networks synchronization mechanism using Generative Adversarial Network (GAN) and mutual learning is used for the development...