Minimally Connective,Auto-associative,Neural Networks

Classic barriers to using auto-associative neural networks to model mammalian memory include the unrealistically high synaptic connectivity of fully connected networks, and the relative paucity of information that has been stored in networks with realistic numbers of synapses per neuron and learning rules amenable to physiological implementation. We describe extremely large, auto-associative networks with low synaptic density. The networks have no direct connections between neurons of the same layer. Rather, the neurons of one layer are 'linked' by connections to neurons of some other layer. Patterns of projections of one layer on to another which form projective planes, or other cognate geometries, confer considerable computational power an the network.     

In search of intelligence: evolving a developmental neuron capable of learning

A neuro-inspired multi-chromosomal genotype for a single developmental neuron capable of learning and developing memory is proposed. This genotype is evolved so that the phenotype which changes and develops during an agent's lifetime (while problem-solving) gives the agent the capacity for learning by experience. Seven important processes of signal processing and neural structure development are identified from biology and encoded using Cartesian Genetic Programming. These chromosomes represent the electrical and developmental aspects of dendrites, axonal branches, synapses and the neuron soma. The neural morphology that occurs by running these chromosomes is highly dynamic. The dendritic/axonal branches and synaptic connections form and change in response to situations encountered in the learning task. The approach has been evaluated in the context of maze-solving and the board game of checkers (draughts) demonstrating interesting learning capabilities. The motivation underlying this research is to, ab initio, evolve genotypes that build phenotypes with an ability to learn.     

Evolving unipolar memristor spiking neural networks

Neuromorphic computing – brain-like computing in hardware – typically requires myriad complimentary metal oxide semiconductor spiking neurons interconnected by a dense mesh of nanoscale plastic synapses. Memristors are frequently cited as strong synapse candidates due to their statefulness and potential for low-power implementations. To date, plentiful research has focused on the bipolar memristor synapse, which is capable of incremental weight alterations and can provide adaptive self-organisation under a Hebbian learning scheme. In this paper, we consider the unipolar memristor synapse – a device capable of non-Hebbian switching between only two states (conductive and resistive) through application of a suitable input voltage – and discuss its suitability for neuromorphic systems. A self-adaptive evolutionary process is used to autonomously find highly fit network configurations. Experimentation on two robotics tasks shows that unipolar memristor networks evolve task-solving controllers faster than both bipolar memristor networks and networks containing constant non-plastic connections whilst performing at least comparably.     

Variable binding by synaptic strength change

Variable binding is a difficult problem for neural networks. Two new mechanisms for binding by synaptic change are presented, and in both, bindings are erased and can be reused. The first is based on the commonly used learning mechanism of permanent change of synaptic weight, and the second on synaptic change which decays. Both are biologically motivated models. Simulations of binding on a paired association task are shown with the first mechanism succeeding with a 97.5% F-Score, and the second performing perfectly. Further simulations show that binding by decaying synaptic change copes with cross talk, and can be used for compositional semantics. It can be inferred that binding by permanent change accounts for these, but it faces the stability plasticity dilemma. Two other existing binding mechanisms, synchrony and active links, are compatible with these new mechanisms. All four mechanisms are compared and integrated in a Cell Assembly theory.     

Creating hierarchical categories using cell assemblies

Highly recurrent neural networks can learn reverberating circuits called Cell Assemblies (CAs). These networks can be used to categorize input, and this paper explores the ability of CAs to learn hierarchical categories. A simulator, based on spiking fatiguing leaky integrators, is presented with instances of base categories. Learning is done using a compensatory Hebbian learning rule. The model takes advantage of overlapping CAs where neurons may participate in more than one CA. Using the unsupervised compensatory learning rule, the networks learn a hierarchy of categories that correctly categorize 97% of the basic level presentations of the input in our test. It categorizes 100% of the super-categories correctly. A larger hierarchy is learned that correctly categorizes 100% of base categories, and 89% of super-categories. It is also shown how novel subcategories gain default information from their super-category. These simulations show that networks containing CAs can be used to learn hierarchical categories. The network then can successfully categorize novel inputs.     

A spiking half-cognitive model for classification

This paper describes a spiking neural network that learns classes. Following a classic Psychological task, the model learns some types of classes better than other types, so the net is a spiking cognitive model of classification. A simulated neural system, derived from an existing model, learns natural kinds, but is unable to form sufficient attractor states for all of the types of classes. An extension of the model, using a combination of singleton and triplets of input features, learns all of the types. The models make use of a principled mechanism for spontaneous firing, and a compensatory Hebbian learning rule. Combined, the mechanisms allow learning to spread to neurons not directly stimulated by the environment. The overall network learns the types of classes in a fashion broadly consistent with the Psychological data. However, the order of speed of learning the types is not entirely consistent with the Psychological data, but may be consistent with one of two Psychological systems a given person possesses. A Psychological test of this hypothesis is proposed.     

A modular approach to self-organization of robot control based on language instruction

In this paper we focus on how instructions for actions can be modelled in a self-organizing memory. Our approach draws from the concepts of regional distributed modularity and self-organization. We describe a self-organizing model that clusters action representations into different locations dependent on the body part they are related to. In the first case study we consider semantic representations of action verb meaning and then extend this concept significantly in a second case study by using actual sensor readings from our MIRA robot. Furthermore, we outline a modular model for a self-organizing robot action control system using language for instruction. Our approach for robot control using language incorporates some evidence related to the architectural and processing characteristics of the brain (Wermter et al. 2001b). This paper focuses on the neurocognitive clustering of actions and regional modularity for language areas in the brain. In particular, we describe a self-organizing network that realizes action clustering (Pulvermüller 2003).     

Flexible feature discovery and structural information control

In this paper, we propose a new information theoretic method called structural information control for flexible feature discovery. The new method has three distinctive characteristics, which traditional competitive learning fails to offer. First, the new method can directly control competitive unit activation patterns, whereas traditional competitive learning does not have any means to control them. Thus, with the new method, it is possible to extract salient features not discovered by traditional methods. Second, competitive units compete witheach other by maximizing their information content about input patterns. Consequently, this information maximization makes it possible to control flexibly competition processes. Third, in structural information control, it is possible to define many different kinds of information content, and we can choose a specific type of information according to a given objective. When applied to competitive learning, structural information can be used to control the number of dead or spare units, and to extract macro as well as micro features of input patterns in explicit ways. We first applied this method to simple pattern classification to demonstrate that information can be controlled and that different neuron firing patterns can be generated. Second, a dipole problem was used to show that structural information could provide representations similar to those by the conventional competitive learning methods. Finally, we applied the method to a language acquisition problem in which networks must flexibly discover some linguistic rules by changing structural information. Especially, we attempted to examine the effect of the information parameter to control the number of dead neurons, and thus to examine how macro and micro features in input patterns can explicitly be discovered by structural information.     

嵌入式网络化交流伺服实验系统的研究

在研究基于工业以太网的现场网络化控制系统结构的基础上,提出了一种基于嵌入式控制器的网络化运动控制实验平台的设计和实现方案.介绍了一种文型高级运动控制语言ELMO Studio 语言及其编译系统.这种语言采用类似C语言的结构形式,包括了 PLC和运动控制器指令,实现了两种指令的统一编程.     

An idea to treat the dendritic morphology in mixed columnar–equiaxed solidification

Abstract

To treat mixed columnar–equiaxed solidification with dendritic morphology, five phase regions have been distinguished: extradendritic melt, interdendritic melt and solid dendrites in equiaxed grains, interdendritic melt and solid dendrites in columnar arrays of dendrites. These five phases are quantified by their volume fractions, and characterized by different volume-averaged solute concentrations. The equiaxed grains and columnar dendrites are confined by their envelopes, whose shapes are described by morphological parameters. The evolution of the envelopes is derived based on recent growth theories: the growth of primary columnar dendrite tips by the Kurz–Giovanola–Trivedi (KGT) model, the growth of secondary dendrite tips in radial direction of columnar trunk and the equiaxed dendrite tips by the Lipton–Glicksman–Kurz (LGK) model. The solidification of the interdendritic melt is governed by diffusion in the interdendritic melt region. Preliminary modelling results on a benchmark casting (Al–4·7wt-% Cu) show the potentials of the model.     

Programming in the brain: a neural network theoretical framework

Recent research shows that some brain areas perform more than one task and the switching times between them are incompatible with learning and that parts of the brain are controlled by other parts of the brain, or are “recycled”, or are used and reused for various purposes by other neural circuits in different task categories and cognitive domains. All this is conducive to the notion of “programming in the brain”. In this paper, we describe a programmable neural architecture, biologically plausible on the neural level, and we implement, test, and validate it in order to support the programming interpretation of the above-mentioned phenomenology. A programmable neural network is a fixed-weight network that is endowed with auxiliary or programming inputs and behaves as any of a specified class of neural networks when its programming inputs are fed with a code of the weight matrix of a network of the class. The construction is based on the “pulling out” of the multiplication between synaptic weights and neuron outputs and having it performed in “software” by specialised multiplicative-response fixed subnetworks. Such construction has been tested for robustness with respect to various sources of noise. Theoretical underpinnings, analysis of related research, detailed construction schemes, and extensive testing results are given.     

基于可视化面向对象建模技术的锻件信息模型的研究

通过对CAD／CAM产品特征建模技术的分析,结合可视化标准建模语言UML的特点,提出了使用可视化标准建模语言UML进行锻件信息模型集成的思路。阐述了锻件信息模型集成的面向对象表达,从而为在塑性加工领域中使用统一的标准建模语言奠定了基础。     

Connectionist learning: A comparison of neural networks and an optical thin-film multilayer model

Current work on connectionist models has been focused largely on artificial neural networks that are inspired by the networks of biological neurons in the human brain. However, there are also other connectionistarchitectures that differ significantly from this biological exemplar. We proposed a novel connectionist learning architecture inspired by the physics associated with optical coatings of multiple layers of thin-films in a previous paper (Li and Purvis 1999, Annals of Mathematics and Artificial Intelligence, 26: 1-4). The proposed model differs significantly from the widely used neuron-inspired models. With thin-film layer thicknesses serving as adjustable parameters (as compared with connection weights in a neural network) for the learning system, the optical thin-film multilayer model (OTFM) is capable of approximating virtually any kind of highly nonlinear mappings. The OTFM is not a physical implementation using optical devices. Instead, it is proposed as a new connectionist learning architecture with its distinct optical properties as compared with neural networks. In this paper we focus on a detailed comparison of neural networks and the OTFM (Li 2001, Proceedings ofINNS-IEEE International Joint Conference on Neural Networks, Washington, DC, pp. 1727-1732). We describe the architecture of the OTFM and show how it can be viewed as a connectionist learning model. We then present experimental results on solving a classification problem and a time series prediction problem that are typical of conventional connectionist architectures to demonstrate the OTFM's learning capability.     

Context-free and context-sensitive dynamics in recurrent neural networks

Continuous-valued recurrent neural networks can learn mechanisms for processing context-free languages. The dynamics of such networks is usually based on damped oscillation around fixed points in state space and requires that the dynamical components are arranged in certain ways. It is shown that qualitatively similar dynamics with similar constraints hold for aⁿbⁿcⁿ , a context-sensitive language. The additional difficulty with aⁿbⁿcⁿ , compared with the context-free language aⁿbⁿ , consists of 'counting up' and 'counting down' letters simultaneously. The network solution is to oscillate in two principal dimensions, one for counting up and one for counting down. This study focuses on the dynamics employed by the sequential cascaded network, in contrast to the simple recurrent network, and the use of backpropagation through time. Found solutions generalize well beyond training data, however, learning is not reliable. The contribution of this study lies in demonstrating how the dynamics in recurrent neural networks that process context-free languages can also be employed in processing some context-sensitive languages (traditionally thought of as requiring additional computation resources). This continuity of mechanism between language classes contributes to our understanding of neural networks in modelling language learning and processing.     

Single Neuron Rational Model of Arithmetic and Logic Operations

This paper will focus on a phase analysis to explore the potential of single neuron local arithmetic and logic operations on their input conductances. The analysis is based on a rational function model of local spatial summation with the equivalent circuits for steadystate membrane potentials. The prototypes of arithmetic and logic operations are then constructed with their input and output range by analyzing the conditions for performing these operations. A mapping from a partition of input conductance space into functionally distinct phases is depicted, and the multiple mode models for arithmetic and logic are then established. This indicates that the single neuron local rational arithmetic and logic is programmable, and the selection of these functional phases can be effectively instructed by presynaptic activities. This programmability makes the single neuron more free to process the input information.     

Modelling process mean and variation with MLP neural networks

Most industrial processes are intrinsically noisy and non-deterministic. To date, most multilayer perceptron (MLP)-based process models were established for process mean only. This paper proposes an approach to modelling the mean and variation of a non-deterministic process simultaneously using a MLP network. The input neurons consist of process variables and one additional neuron for the Z value. The corresponding output responses are calculated based on , s_p/√k.

The process variance s_p² is determined by pooling the individual sample variances for k experimental conditions. Each sample variance is calculated from the replicated data. The effects of a number of hidden neurons and learning algorithms are studied. Two learning algorithms are applied. They are the back-propagation with momentum (BPM) and Fletcher-Reeves (FR) algorithms. The effectiveness of the proposed approach is tested with a fictitious process and an actual manufacturing process. The test results are provided and discussed.     

基于模糊神经网络的冷连轧机轧制力预测

建立了一种基于数学模型和模糊神经网络共同作用的冷连轧机轧制力预测模型,通过数学模型描述轧制接触面积,模糊网络预测轧制单位压力.提出将Hough变换应用于神经网络的参数确定,从而使最终设计的网络具有最佳结构参数.试验研究证明了所设计模型具有较强的泛化能力和鲁棒性,大大提高了轧制力的预报精度.     

High strain rate compressive behavior of Ti-based metallic glass matrix composites

The compression behaviors of Ti-based metallic glass matrix composites with dendrites scale were tested at different loading rates. It was found that the composites exhibited not only high strength, but also large plasticity under quasi-static compression. Under the dynamic loading, however, the TZ1 alloy with fine dendrites demonstrated a catastrophic failure. Although both the strength and plasticity decreased for the TZ2 composite sample with coarse dendrite, the total strain is over 7%. Discussions on the strain rates and dendrite scale are provided by analyzing the effects of dendrite, which can present the possible deformation mechanism of the composites.     

应用神经网络辅助计算工时定额的方法研究

针对企业传统工时定额制定方法存在效率低、准确度不高、不能与生产实践紧密联系等问题,提出一种数学模型与神经网络相结合制定工时的方法.以标准工时定额表中的计算公式为基础建立数学模型,应用神经网络来确定数学模型中的切削用量,构建一个与CAPP系统紧密结合的工时定额系统.采用Matlab脚本语言构建网络模型,该方法简单、实用,并能根据企业的现场环境修正计算结果,满足企业实际的生产需求.