一种基于神经网络的不确定知识获取方法

本文介绍一种基于神经网络学习算法获取专家系统不确定知识的方法。文中给出神经网络的连接网络与专家系统的推理网络之间的可转换性,以及证明了它们的权值构成的学习矩阵与可信度构成的规则矩阵之间存在着一种等价性的关系。     

基于神经网络的符号知识获取方法

本文基于神经网络的知识获取研究进行了综述,介绍了几种有效的模型和方法,并通过比较分析,提出了进一步开展研究工作的意见和看法。     

一种基于符号神经网络的知识获取方法

本文介绍一种基于符号神经网络的知识获取方法，该方法首先用传统的机器学习方法获取关于某领域的粗略知识，然后把这些知识映射到神经网络结构，通过神经网络的自学习获取关于该领域的精细知识，这样，既解决了传统机器学习中知识精度，知识表示等问题，又解决了神经网络获取知识时间长，能释能力弱等问题。     

基于神经网络的知识获取

本文提出了用基于规则专家系统与神经网络的集成，该系统实现了从实例中自动获取知识的功能．在产生和控制不完全情况方面提高了专家系统的推理能力．它使用无导师学习算法的神经网络来获取正规数据，并用一个符号生成器把这些正规的数据变换成规则．生成规则和训练后的神经网络作为知识库嵌于专家系统中．在诊断阶段，为了诊断不明情况，可同时使用知识库和人类专家的知识，而且系统可以利用训练过的神经网络的综合能力进行诊断，并使不相符数据完整化．     

一种基于神经网络的知识获取方法研究与应用

提出了一种基于神经网络的知识获取方法,该方法利用语言神经元,对具有开区域的连续输入变量,自动产生相应的语言变量输出,讨论了相应的神经网络训练和知识获取方法,所获取的知识以If-Then的规则形式表示,具有简洁、紧凑、不必进一步化简、易于理解等特点,并给出一个在教学型专家系统中获取专家领域知识的应用实例。     

基于BP神经网络在专家系统知识获取上的应用

提出电力线路故障诊断专家系统中基于BP神经网络知识获取的方法,利用神经网络的自学习功能,解决知识更新的问题,并在一定程度上弥补了专家系统在知识获取上的不足。     

基于概念格的数据驱动不确定知识获取

在没有领域先验知识的条件下,不确定知识获取是机器学习研究中的一个难题.本文利用决策表和决策规则的不确定性,通过分析决策表、决策规则及概念格的知识表示形式,发现这3种知识表示形式中知识不确定性之间的关系,进而提出基于概念格的数据驱动不确定知识获取算法.仿真实验结果表明,该算法在不确定性知识获取中是有效的.     

基于神经网络的交互式自动获取知识的N－R方法

本文为解决知识系统构造过程中的瓶颈问题──知识获取,提出了一种基于神经网络ＮＮ（ｎｅｕｒａｌｎｅｔｗｏｒｋ）的自动获取多级推理产生式规则的Ｎ－Ｒ方法,该方法采用了特有的ＮＮ结构模型和相应的学习算法,使得ＮＮ在学习过程中动态确定隐层节点数的同时,也产生了样例集中没有定义的新概念,学习后的ＮＮ能用本文提出的转换算法转换成推理网络,最终方便地得到产生式规则集.     

一种基于模糊Petri网的不确定知识获取方法及其应用

本文介绍一种基于模糊Petri网获取专家系统不确定知识的方法,文中给出了模糊Petri网的知识表示、不确定推理算法及应用实例。     

神经网络的知识获取与行为解释

神经网络的缺点之一在于它无法明显地表达其行为的含义.针对神经网络的不同结构,通过给出节点取值不同的约束条件,描述了如何从神经网络提取知识,并对神经网络的行为进行解释.举例说明了从神经网络提取产生式规划的策略.     

前向代数神经网络的函数逼近理论及学习算法

文中对ＭＰ神经元模型进行了推广,定义了多项代数神经元、多项式代数神经网络,将多项式代数融入代数神经网络,分析了前向多项式代数神经网络函数逼近能力及理论依据,设计出了一类双输入单输出的前向４层多层式代数神经网络模型,由该模型构成的网络能够逼近于给定的二元多项式到预定的精度。给出了在Ｐ－ａｄｉｃ意义下的多项式代数神经网络函数逼近整体学习算法,在学习的过程中,不存在局部极小,通过实例表明,该算法有效,最     

Short term memory in recurrent networks of spiking neurons

We present in this paper a general model of recurrent networks of spiking neurons, composed of several populations, and whose interaction pattern is set with a random draw. We use for simplicity discrete time neuron updating, and the emitted spikes are transmitted through randomly delayed lines. In excitatory-inhibitory networks, we show that inhomogeneous delays may favour synchronization provided that the inhibitory delays distribution is significantly stronger than the excitatory one. In that case, slow waves of synchronous activity appear (this synchronous activity is stronger in inhibitory population). This synchrony allows for a fast ada ptivity of the network to various input stimuli. In networks observing the constraint of short range excitation and long range inhibition, we show that under some parameter settings, this model displays properties of –1– dynamic retention –2– input normalization –3– target tracking. Those properties are of interest for modelling biological topologically organized structures, and for robotic applications taking place in noisy environments where targets vary in size, speed and duration. This revised version was published online in June 2006 with corrections to the Cover Date.     

Acquiring,storing and utilizing process planning knowledge using neural networks

An approach is formulated for the automated acquisition of process selection and within-feature process sequencing knowledge from examples using neural networks. Network architecture, problem representation and performance issues are discussed.     

Recurrent neural networks and prior knowledge for sequence processing: a constrained nondeterministic approach

The paper focuses on methods for injecting prior knowledge into adaptive recurrent networks for sequence processing. In order to increase the flexibility needed for specifying partially known rules, a nondeterministic approach for modelling domain knowledge is proposed. The algorithms presented in the paper allow time-warping nondeterministic automata to be mapped into recurrent architectures with first-order connections. These kinds of automata are suitable for modeling temporal scale distortions in data such as acoustic sequences occurring in problems of speech recognition. The algorithms output a recurrent architecture and a feasible region in the connection weight space. It is demonstrated that, as long as the weights are constrained into the feasible region, the nondeterministic rules introduced using prior knowledge are not destroyed by learning. The paper focuses primarily on architectural issues, but the proposed method allows the connection weights to be subsequently tuned to adapt the behavior of the network to data.     

A class of discrete time recurrent neural networks with multivalued neurons

This paper discusses a class of discrete time recurrent neural networks with multivalued neurons (MVN), which have complex-valued weights and an activation function defined as a function of the argument of a weighted sum. Complementing state-of-the-art of such networks, our research focuses on the convergence analysis of the networks in synchronous update mode. Two related theorems are presented and simulation results are used to illustrate the theory.     

VLSI implementation of pulsating neural networks

We describe a generic approach for realizing networks of pulsating neurons based on charge pumping of interface states situated in the channel of MOS transistors. Two basic building blocks will be described: the pulse activated charge pumping (PSCP) synapse, and the charge sensitive oscillator (CSO). The PSCP synapse which operates as either a short or a long term memory device which produces a charge packet proportional to the number of pulses applied to its input, will be described in detail together with experimental results demonstrating its capability. The CSO circuit which is a charge controlled oscillator will be described together with simulations of its output frequency dependence on its input voltage, and the relation between the temporal dependence of output waveform on its input charge.     

A design and analysis of objective function-based unsupervised neural networks for fuzzy clustering

Fuzzy clustering has played an important role in solving many problems. In this paper, we design an unsupervised neural network model based on a fuzzy objective function, called OFUNN. The learning rule for the OFUNN model is a result of the formal derivation by the gradient descent method of a fuzzy objective function. The performance of the cluster analysis algorithm is often evaluated by counting the number of crisp clustering errors. However, the number of clustering errors alone is not a reliable and consistent measure for the performance of clustering, especially in the case of input data with fuzzy boundaries. We introduce two measures to evaluate the performance of the fuzzy clustering algorithm. The clustering results on three data sets, Iris data and two artificial data sets, are analyzed using the proposed measures. They show that OFUNN is very competitive in terms of speed and accuracy compared to the fuzzy c-means algorithm.     

神经元网络软测量技术的进展

软测量技术已经在过程控制理论研究和实践中产生了广泛的影响，但是目前尚未形成系统的理论。近年来神经元网络软测量技术有了一定发展，在训练算法和建立模型方面都取得了可喜的进展。讨论了近年来神经网络软测量技术的一些研究新进展，并介绍了其与控制技术、计算机通信、虚拟仪器及WWW结合的应用实例。     

基于知识的车徽识别方法

车徽识别是车辆自动识别技术一个新的研究方向,它对智能交通中车辆识别技术的完善和发展具有重要的理论意义与应用价值.对多种车徽进行了研究,提出了一种基于知识的车徽识别方法.该方法以专家系统理论为理论基础,用车徽的间隙数、轮廓特征、神经网络权值以及车徽识别的规则来建立知识库,利用推理机进行车徽的识别.实验结果证明,该方法识别速度快.识别率高.     

Using single-layered neural networks for the extraction of conjunctive rules and hierarchical classifications

Machine Learning is an area concerned with the automation of the process of knowledge acquisition. Neural networks generally represent their knowledge at the lower level, while knowledge based systems use higher level knowledge representations. The method we propose here, provides a technique which automatically allows us to extract production rules from the lower level representation used by a single-layered neural networks trained by Hebb's rule. Even though a single-layered neural network can not model complex, nonlinear domains, their strength in dealing with noise has enabled us to produce correct rules in a noisy domain.