A defence of functional modularity

Although belief in the existence of mental modules of some form is widespread among cognitive researchers, neurally sophisticated researchers commonly resist the view that cognitive processing involves modules that are functionally independent of one another. Moreover, within the past few years, at least three noted researchers (Fodor, Kosslyn and Uttal) have called into serious question the existence of distinct modules in broad areas of human cognition. This paper offers a defence of the existence of functionally independent modules, which, though spatially distributed, communicate via traditionally conceived input/output channels. This defence proceeds: (i) by showing that the anti-modularity arguments of Fodor, Kosslyn and Uttal are not compelling; (ii) by presenting theoretically-grounded reasons why any connectionist is committed, via the most basic tenets of connectionism, to accepting the existence of functionally independent modules; (iii) by presenting holistically inclined connectionists with a novel challenge, namely, to demonstrate that a single, holistic network could display strong levels of generalization as a side-effect of multiple, previously acquired skills. In the course of these arguments, I examine a recent generalization challenge posed by Phillips (2000, Connection Science, 12: 1–19) to eliminative connectionists.     

The cultural evolution of communication in a population of neural networks

Human language is learned, symbolic and exhibits syntactic structure, a set of properties which make it unique among naturally-occurring communication systems. How did human language come to be as it is? Language is culturally transmitted and cultural processes may have played a role in shaping language. However, it has been suggested that the cultural transmission oflanguage is constrained by some language-specific innate endowment. The primary objective of the research outlined in this paper is to investigate how such an endowment would influence the acquisition of langage and the dynamics of the repeated cultural transmission of language. To this end, a new connectionist model of the cultural evolution of communication is presented. In this model an individual's innate endowment is considered to be a learning rule with an associated learning bias. The model allows manipulations to be made to this learning apparatus andthe impact of such manipulations on the processes of language acquisition and language evolution to be explored. These investigations reveal that an innate endowment consisting of an ability to read the communicative intentions of others and a bias towards acquiring one-to-one mappings between meanings and signals results in the emergence, through purely cultural processes, of optimal communication. It has previously been suggested that humans possess just such an innate endowment. Properties of human language may therefore best be explained in terms of cultural evolution on an innate substrate.     

Reuse of information in multi-layer sigma-pi neural networks

This new work is an extension of existing research into artificial neural networks (Neville and Stonham, Connection Sci.: J. Neural Comput. Artif. Intell. Cognitive Res., 7, pp. 29–60, 1995; Neville, Neural Net., 45, pp. 375–393, 2002b). These previous studies of the reuse of information (Neville, IEEE World Congress on Computational Intelligence, 1998b, pp. 1377–1382; Neville and Eldridge, Neural Net., pp. 375–393, 2002; Neville, IEEE World Congress on Computational Intelligence, 1998c, pp. 1095–1100; Neville, IEEE 2003 International Joint Conference on Neural Networks, 2003; Neville, IEEE IJCNN'04, 2004 International Joint Conference on Neural Networks, 2004) are associated with a methodology that prescribes the weights, as opposed to training them. In addition, they work with smaller networks. Here, this work is extended to include larger nets. This methodology is considered in the context of artificial neural networks: geometric reuse of information is described mathematically and then validated experimentally. The theory shows that the trained weights of a neural network can be used to prescribe the weights of other nets of the same architecture. Hence, the other nets have prescribed weights that enable them to map related geometric functions. This means the nets are a method of ‘reuse of information’. This work is significant in that it validates the statement that, ‘knowledge encapsulated in a trained multi-layer sigma-pi neural network (MLSNN) can be reused to prescribe the weights of other MLSNNs which perform similar tasks or functions’. The important point to note here is that the other MLSNNs weights are prescribed in order to represent related functions. This implies that the knowledge encapsulated in the initially trained MLSNN is of more use than may initially appear.     

Self-refreshing memory in artificial neural networks: learning temporal sequences without catastrophic forgetting

While humans forget gradually, highly distributed connectionist networks forget catastrophically: newly learned information often completely erases previously learned information. This is not just implausible cognitively, but disastrous practically. However, it is not easy in connectionist cognitive modelling to keep away from highly distributed neural networks, if only because of their ability to generalize. A realistic and effective system that solves the problem of catastrophic interference in sequential learning of ‘static’ (i.e. non-temporally ordered) patterns has been proposed recently (Robins 1995 Robins, AV. 1995. Catastrophic forgetting, rehearsal and pseudorehearsal. Connection Science, 7: 123–146. [Taylor & Francis Online] [Google Scholar], Connection Science, 7: 123–146, 1996, Connection Science, 8: 259–275, Ans and Rousset 1997 Ans, B and Rousset, S. 1997. Avoiding catastrophic forgetting by coupling two reverberating neural networks. CR Académie des Sciences Paris, Life Sciences, 320: 989–997.  [Google Scholar], CR Académie des Sciences Paris, Life Sciences, 320: 989–997, French 1997 French, RM. 1997. Pseudo-recurrent connectionist networks: an approach to the ‘sensitivity–stability’ dilemma. Connection Science, 9: 353–379. [Taylor & Francis Online] [Google Scholar], Connection Science, 9: 353–379, 1999, Trends in Cognitive Sciences, 3: 128–135, Ans and Rousset 2000 Ans, B and Rousset, S. 2000. Neural networks with a self-refreshing memory: knowledge transfer in sequential learning tasks without catastrophic forgetting. Connection Science, 12: 1–19. [Taylor & Francis Online], [Web of Science ®] [Google Scholar], Connection Science, 12: 1–19). The basic principle is to learn new external patterns interleaved with internally generated ‘pseudopatterns’ (generated from random activation) that reflect the previously learned information. However, to be credible, this self-refreshing mechanism for static learning has to encompass our human ability to learn serially many temporal sequences of patterns without catastrophic forgetting. Temporal sequence learning is arguably more important than static pattern learning in the real world. In this paper, we develop a dual-network architecture in which self-generated pseudopatterns reflect (non-temporally) all the sequences of temporally ordered items previously learned. Using these pseudopatterns, several self-refreshing mechanisms that eliminate catastrophic forgetting in sequence learning are described and their efficiency is demonstrated through simulations. Finally, an experiment is presented that evidences a close similarity between human and simulated behaviour.     

An evaluation of recurrent neural network modelling for the prediction of damage evolution during forming

This paper examines the efficiency and capability of Dynet, a recurrent neural network model for the prediction of the damage evolution during hot non-uniform, non-isothermal forging on the basis of a limited number of damage snapshots during the process. A Bayesian algorithm is introduced to optimise the hyperparameters related to the noise level and weight decay. In order to examine the capability of the model to capture the underlying trends when presented with sparse and noisy evidence, a synthetic relation between damage accumulation in a metal matrix composite and strain, strain rate and deformation temperature has been used to generate training data (evidence) of varying accuracy and sparseness. The results show that the Bayesian algorithm performs very well, and that no significant overfitting is observed. In addition, this algorithm not only gives the expectation value of damage level, but also an estimate of its uncertainty.     

钦州那雾山地质遗迹形成演化过程及保护对策研究

文章以钦州那雾山地质遗迹为例,通过实地综合科学考察、系统整理、归纳分析、定性评价等,初步查明了钦州那雾山地质遗迹特征,分析了地质遗迹形成演化过程,提出了地质遗迹保护对策,为后续地质遗迹保护工作提供参考.     

人工神经网络在过程工业中的应用

当前,集过程实时监测、故障诊断、模拟、优化、控制以及调度等各层次功能于一体的过程工业生产过程综合自动化成了过程工业界和学术界共同关注的热点之一.与离散产品的制造业相比,由于流程型工业过程具有强非线性的特点,给实现流程工业综合自动化造成很大的困难,因此必须引入新的思路,开发新的方法.人工神经网络是一种模拟人类思维活动的并行分布式的信息处理系统,可用于映射任何连续函数及进行模式识别,同时还具有自学习功能,实现知识的自动获取,自20世纪90年代以来已在过程系统工程领域内受到广泛的瞩目.重点讨论了人工神经网络在过程系统建模、故障诊断以及在线优化等方面的应用,以展示这种方法在流程工业综合自动化中的良好应用前景.     

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.     

Artificial neural networks in steel-mushy aluminum pressing bonding

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Stack-like and queue-like dynamics in recurrent neural networks

What dynamics do simple recurrent networks (SRNs) develop to represent stack-like and queue-like memories? SRNs have been widely used as models in cognitive science. However, they are interesting in their own right as non-symbolic computing devices from the viewpoints of analogue computing and dynamical systems theory. In this paper, SRNs are trained on two prototypical formal languages with recursive structures that need stack-like or queue-like memories for processing, respectively. The evolved dynamics are analysed, then interpreted in terms of simple dynamical systems, and the different ease with which SRNs aquire them is related to the properties of these simple dynamical systems. Within the dynamical systems framework, it is concluded that the stack-like language is simpler than the queue-like language, without making use of arguments from symbolic computation theory.     

Effects of compressing and remelting in SIMA processing on semi-solid structure evolution of an Al-Zn wrought alloy

Structure evolution of an Al-Zn wrought alloy in remelting processing in the strain induced melt activated (SIMA) semi-solid procedure was observed, and effects of factors, the remelting temperature, the holding time, and the compression strain, on structures and grain sizes of the alloy were investigated. The results show that (1) the proper temperature of remelting is in the range of 610 to 615 ℃; (2) the grain size in specimen with greater compression strain is smaller than that with smaller compression strain in condition of the same remelting temperature and holding time, and the grain size in local area with great local equivalent strain is smaller than that with small one; (3) liquid occurs in form of cluster in matrix during remelting and its quantity increases with remelting time increasing; liquid in specimen with great compression strain occurs earlier than that with small one, and quantity of liquid in the center of specimen with greater local equivalent strain is greater man that in the tw     

300M钢的热变形行为及其变形组织演变研究

基于热压缩实验,对300M钢在应变速率为10 s-1下的热变形行为及其变形组织演变进行了研究.结果表明:在试样高度压下量为50％,变形温度为700～750℃时,300M钢的应力-应变曲线呈流变失稳型,且变形组织出现绝热剪切;当变形温度为800～1000℃时,300M钢的应力-应变曲线呈双峰不连续动态再结晶型,且热变形过程出现了两轮动态再结晶;当变形温度为1050～1180℃时,300M钢的应力-应变曲线呈单峰不连续动态再结晶型,且热变形过程只发生了一轮动态再结晶.     

模块化设计在机械压力机设计中的应用探讨

模块化设计在电子电器和IT行业已发展多年,但在通用机械行业,因其结构、产量、技术沉淀、行业特点等原因,并未得到广泛应用。本文力求用简洁明了的语言,对模块化设计的概念和程序进行阐述,推动模块化设计在机械行业的应用。     

Signal processing in defect detection using back-propagation neural networks

The process of teaching and testing of back-propagation neural networks to characterize defect signals and tilt of eddy-current probe signals by superimposing different noise levels has been analyzed. Results of classification by clustering have been considered. Using numerical simulation for justification, it has been shown that clustering increases the probability of signal recognition.     

Control of consolidation in neural networks: Avoiding runaway effects

Consolidation has been implemented in two ways: as straight rehearsal of patterns or as pseudorehearsal, in which pseudoitems are created by sampling attractors or input-output combinations from the network. Although both implementations have been investigated by several authors, few have explored how it is decided which pattern or pseudoitem is consolidated. Controlling consolidation is not trivial, as it is susceptible to a corruption. In runaway consolidation, one or two patterns monopolize all consolidation resources and come to dominate the entire network. Runaway consolidation is analysed, and three solutions are explored. Suppressing transmission in the connections in which consolidation takes place is shown to work best. Placing bounds on connections or unlearning attractors also alleviates runaway consolidation, though less effectively so.     

The application of neural networks in self-tuning constant force control

The constant force control gradually becomes an important technique of modern manufacturing processes. For example, the constant turning or cutting force is a useful approach for increasing the metal removal rate and increasing the tool life. The variation of machining condition may cause the robustness of a classical control theory (PID) to become ineffective, even make a control system unstable. The pole placement self-tuning control (PSTC) theory with a recursive least square parameters estimator is proposed to adapt the environmental variety. Unfortunately, the adaptability and the robustness of a self-tuning control system cannot be maintained in good condition simultaneously all the time. In this paper, a self-tuning controller equips with a neural network parameter classifier in conjunction with a least square estimator is developed to improve the adaptability and the robustness in suffering the obvious environmental variation. In order to verify the applicability of this control method, a prototype system is designed and constructed to resemble the feed rate mechanism of lathe. The dynamic responses of this force control system with different estimators are compared based upon the experimental data. The contact force is measured from a load cell and adjusted by regulating the feed rate.     

Structural evolution of ZA27 alloy during semi-solid isothermal heat treatment

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Using image processing based on neural networks in reverse engineering

Various techniques have been employed to recover three-dimensional (3D) surfaces in the redesign of products, customized designs, and the building of virtual environments. Reverse engineering methodology provides an efficient tool for the manufacturing of free-form and sculptured shapes. This paper describes an image processing approach to the 3D shape recovery based on neural networks that tackles the major bottleneck in the current reverse engineering process, namely the lack of a rapid link between the physical object and its design representation. A range of applications has shown this method to be feasible and efficient.     

Atomic simulation on evolution of nano-crystallizaion in amorphous metals

The deformation-induced nano-crystallization behavior of amorphous pure Ni was investigated by using a molecular dynamics simulation. The microevolution mechanism of the nano-crystallization, the crystallization process in the multicomponent amorphous Ni-Pd alloys and the temperature effect on the nano-crystallization behavior in amorphous metals were studied. The results show that the small nano-crystalline grain will nucleate and grow during the compression deformation. The deformation induces the growth of the ordered clusters in the amorphous metals and the nano-crystalline grain grows under the shearing combination and sheafing deposition. The nano-crystalline grain will nucleate in a lower strain under a higher temperature. The combining severe plastic deformation with thermal annealing treatments presents a new opportunity for developing bulk nano-crystalline materials with controlled microstructures.     

Teacher-directed learning: information-theoretic competitive learning in supervised multi-layered networks

In this paper, we propose a new type of efficient learning method called teacher-directed learning. The method can accept training patterns and correlated teachers, and we need not back-propagate errors between targets and outputs into networks. Information flows always from an input layer to an output layer. In addition, connections to be updated are those from an input layer to the first competitive layer. All other connections can take fixed values. Learning is realized as a competitive process by maximizing information on training patterns and correlated teachers. Because information is maximized, information is compressed into networks in simple ways, which enables us to discover salient features in input patterns. We applied this method to the vertical and horizontal lines detection problem, the analysis of US–Japan trade relations and a fairly complex syntactic analysis system. Experimental results confirmed that teacher information in an input layer forces networks to produce correct answers. In addition, because of maximized information in competitive units, easily interpretable internal representations can be obtained.