Hopfield-Like Neural Nets and Sensor Networks

The transformation of a sensor network (SN) into a neural Hopfield-like network (HLN) is proposed. The SN of interest is a nonlinear non-reciprocal population of coupled oscillators. The proposed transformation is useful for investigating the relation between the structure of the SN and its capability of arriving to a global consensus. The case of a 3-SN is developed in detail for illustrating the advantages of the suggested transformation. Both the structural conditions necessary for achieving in this case the consensus and its relation to local measurements are presented.     

Discovering Text Databases with Neural Nets

Since documents on the Web are naturally partitioned into many text databases, the efficient document retrieval process requires identifying the text databases that are most likely to provide relevant documents to the query and then searching for the identified text databases. In this paper, we propose a neural net based approach to such an efficient document retrieval. First, we present a neural net agent that learns about underlying text databases from the user's relevance feedback. For a given query, the neural net agent, which is sufficiently trained on the basis of the BPN learning mechanism, discovers the text databases associated with the relevant documents and retrieves those documents effectively. In order to scale our approach with the large number of text databases, we also propose the hierarchical organization of neural net agents which reduces the total training cost at the acceptable level. Finally, we evaluate the performance of our approach by comparing it to those of the conventional well-known approaches. Received 5 March 1999 / Revised 7 March 2000 / Accepted in revised form 2 November 2000     

Feedback Control Logic for Backward Conflict Free Choice Nets

This paper discusses the forbidden state problem, as specified by generalized mutual exclusion constraints, in the context of supervisory control of discrete event systems modelled by Petri nets. The case of backward-conflict-free and free-choice uncontrollable subnets is considered and it is shown how to transform such subnets in well-formed free-choice nets. Then, the well-formed free-choice nets are decomposed in marked graph components by recurring to minimal T-invariants. The forbidden state problem is so reformulated for the obtained marked graph components into an equivalent one which is shown to be a linear programming problem. Thus, improving existing results in literature, a polynomial complexity solution, suitable for on-line control, is achieved. Free-choice relationship and cycle modelling, that frequently occur in real-life situations, are so allowed in the uncontrollable subnet     

Neural Nets: An Evaluation and a Spreadsheet Implementation

Attitudes to neural nets range from suspicion to uncritical admiration. This paper aims to introduce nets and to evaluate their strengths and weaknesses. The language is non-technical, but the conceptual treatment is intended to be rigorous. A practical method for implementing a neural net on a spreadsheet is described, and sample results illustrated.     

Incremental Evolution in ANNs: Neural Nets which Grow

This paper explains the optimisation of neuralnetwork topology using Incremental Evolution;that is, by allowing the network to expand byadding to its structure. This method allows anetwork to grow from a simple to a complexstructure until it is capable of fulfilling itsintended function. The approach is somewhatanalogous to the growth of an embryo or theevolution of a fossil line through time, it istherefore sometimes referred to as anembryology or embryological algorithm. Thepaper begins with a general introduction,comparing this method to other competingtechniques such as The Genetic Algorithm, otherEvolutionary Algorithms and SimulatedAnnealing. A literature survey of previous workis included, followed by an extensive newframework for application of the technique.Finally, examples of applications and a generaldiscussion are presented.     

Information and Meaning: Use-Based Models in Arrays of Neural Nets

The goal of philosophy of information is to understand what information is, how it operates, and how to put it to work. But unlike information in the technical sense of information theory, what we are interested in is meaningful information. To understand the nature and dynamics of information in this sense we have to understand meaning. What we offer here are simple computational models that show emergence of meaning and information transfer in randomized arrays of neural nets. These we take to be formal instantiations of a tradition of theories of meaning as use. What they offer, we propose, is a glimpse into the origin and dynamics of at least simple forms of meaning and information transfer as properties inherent in behavioral coordination across a community.     

Refractoriness in Poisson and Gaussian First-order Neural Nets with Chemical Markers

In this work first order probabilistic Poisson and Gaussian neural nets with chemical markers are investigated, analytically and by computer simulations. The investigation of steady-state behavior of these systems is extended here to systems in which the refractory period is assigned to be 1 for all or some of the subpopulations of the net, whereas the remainder are characterized by zero refractory periods. The interest is focused on the effects of refractoriness on the neural activities. Results obtained show the existence of several critical points at high initial activities, which are a consequence of the nonzero refractory periods. For these points a larger initial activity, above a certain critical level, results in the reduction of activity to a lower stable steady-state, instead of the highest one. We also find that in the Gaussian nets each critical point is lower than the corresponding one as in the Poisson nets. Finally, a discussion of the results is made.     

一种神经网络判决反馈均衡器

在分析Chebyshev正交多项式神经网络非线性滤波器的基础上,利用判决反馈均衡器的结构特点,提出了一种Chebyshev正交多项式神经网络判决反馈均衡器,给出了对应的自适应NLMS算法.数值仿真结果表明,该均衡器结构能够更有效地消除码间和非线性干扰,降低信号的误码率.     

Neural Nets Trained by Genetic Algorithms for Collision Avoidance

As air traffic keeps increasing, many research programs focus on collision avoidance techniques. For short or medium term avoidance, new headings have to be computed almost on the spot, and feed forward neural nets are susceptible to find solutions in a much shorter amount of time than classical avoidance algorithms (A^*, stochastic optimization, etc.) In this article, we show that a neural network can be built with unsupervised learning to compute nearly optimal trajectories to solve two aircraft conflicts with the highest reliability, while computing headings in a few milliseconds.     

Probabilistic and Other Neural Nets in Multi-Hole Probe Calibration and Flow Angularity Pattern Recognition

The use of probabilistic (PNN) and multilayer feedforward (MLFNN) neural networks is investigated for the calibration of multi-hole pressure probes and the prediction of associated flow angularity patterns in test flow fields. Both types of network are studied in detail for their calibration and prediction characteristics. The current formalism can be applied to any multi-hole probe, however the test results for the most commonly used five-hole Cone and Prism probe types alone are reported in this paper. Received: 1 October 1998?Received in revised form: 12 December 1998?Accepted: 16 December 1998     

广义连续反馈型神经网络的稳定性

通过对连续Hopfield网络模型的抽象,提出了广义连续反馈型神经网络的概念,指出一种不需要构造能量函数对网络模型的稳定性进行分析和判别的方法,同时给出了相应的数学证明。     

Adaptive Feedback Linearization Using Efficient Neural Networks

For a class of single-input, single-output, continuous-time nonlinear systems, a feedback linearizing neural network (NN) controller is presented. Control action is used to achieve tracking performance. The controller is composed of a robustifying term and two neural networks adapted on-line to linearize the system by approximating two nonlinear functions. A stability proof is given in the sense of Lyapunov. No off-line weight learning phase is needed and initialization of the network weights is straightforward. The NN controller is tested on a standard benchmark problem.     

Approximate Feedback Linearisation Using Multilayer Neural Networks

In this paper we introduce the approximate feedback linearisation using multilayer feedforward neural networks. We propose to approximate a basis of the one-dimensional codistribution of an affine nonlinear system with the derivative of a multilayer neural network [6] and form a change of coordinates with n multilayer neural networks [5]. In this paper we will prove that the transformation can define a local diffeomorphism, with which a local stabilising feedback law can be designed for a kind of non-linearisable nonlinear systems.     

人工神经网络研究与发展综述

本文综述了神经网络理论发展的历史和现状,讨论了人工神经网络的两个主要研究方向:神经网络的VC维计算和神经网络的数据挖掘,也介绍了神经网络计算理论、方法、应用等不同层面的一些重要研究领域。     

基于模糊神经Petri网的故障诊断模型

Petri网是对具有产生式规则的故障诊断系统的有力建模工具,但其缺乏较强的学习能力．本文以Petri网的基本定义为基础,结合模糊逻辑和Petri网模型,定义了模糊Petri网模型,在此基础上引入人工神经网络技术,给出了人工神经网络的模糊Petri网表示方法,并针对工程机械故障诊断异步、离散等特点,提出并建立了故障诊断的模糊神经Petri网模型及其改进模型．基于模糊神经Petri网的故障诊断系统结合了Petri网和人工神经网络的优点,经过自学习后同时具有很强的推理能力和自适应能力．     

特征识别-反馈型神经网络设计

构造一个Elman反馈神经网络来进行模式识别,给出了所构造的Elman反馈神经网络的结构,它相对于BP网络的优势在于它能在有限时间内以任意精度逼近任意函数,在错误概率最小的条件下,使识别的结果尽量与客观事物相符。对二者的识别错误率进行比较,结果表明,反馈神经网络在模式识别的稳定性及真实性上有着BP网络所无法比拟的优势。对如何提高反馈神经网络的辨识精度做了一些探讨。     

基于反馈神经网络的图像盲分离

介绍基于神经网络的盲源分离算法的原理和结构,针对基于最小互信息后馈神经网络,建立了新的盲源分离算法,比起简单的独立分量分析,该算法可以更有效地对混合图像进行分离,计算机仿真实验验证了该算法的优良性能.     

反馈RBF神经网络在动态系统建模中的应用

实际过程对象一般是动态非线性系统,然而前向神经元网络很难对动态系统进行建模,为解决这一问题,在RBF网络中引入输出反馈,使其适用于动态系统建模.为更有效地确定反馈RBF网络隐含层节点的个数,引入样本密度以及样本与输出目标的关联度,用较少的神经元实现网络的训练目标.仿真结果表明反馈RBF网络具有训练快,对样本需求少等特点;与其他建模方法的比较以及对实际对象的建模表明,反馈RBF网络对动态非线性系统建模是有效的、可行的.     

Nonlinear Control VIA Generalized Feedback Linearization Using Neural Networks

A novel approach to nonlinear control, called Generalized Feedback Linearization (GFL), is presented. This new strategy overcomes one important drawback of the well known Feedback Linearization strategy, in the sense that it is able to handle a broader class of nonlinear systems, namely those having unstable zero dynamics. It is shown that the use of a nonlinear predictor for the system output is a key feature in the derivation of the control strategy. For certain types of systems this predictor can be found as a nonlinear function of the system input and output, allowing an output feedback control solution. The use of Artificial Neural Networks (ANN) to directly parameterize the predictor of the controlled variable when an explicit model for the system is not available, is investigated via computer simulations. This approach is based on the functional approximation capability of multi layer ANN.