基于FAM的模糊神经控制器的研究

根据模糊联想记忆(FAM)理论, 提出了预解模糊FAM原理, 给出了预解模糊FAM和一般FAM的等价性的构造性证明. 为了提高FAM推理过程的自适应能力, 将神经网络应用于预解模糊FAM推理, 提出了一种新的智能控制器——FAM神经控制器(FAMNC), 以小车倒立摆为控制对象进行了仿真研究, 表明了所提方法的可行性.     

多层前馈神经网络在基于案例推理的应用

基于案例的推理（CBR）系统的增量式学习会使案例库逐渐增大,导致案例的检索时间较长,效率较低。多层前馈神经网络是构造性神经网络技术,很容易构筑及理解,具有较低的时间和空间复杂性和较高的识别率。利用该神经网络技术对案例库进行分类后,待求解的新问题只需在某个子案例库中进行检索,便可以有效地解决大规模案例库的能力与效率的维护问题,确保CBR系统的能力保护与效率保护兼顾的实现,为大规模案例库的应用提供技术保证。     

Interval type-2 fuzzy logic and modular neural networks for face recognition applications

In this paper we present a method for response integration in multi-net neural systems using interval type-2 fuzzy logic and fuzzy integrals, with the purpose of improving the performance in the solution of problems with a great volume of information. The method can be generalized for pattern recognition and prediction problems, but in this work we show the implementation and tests of the method applied to the face recognition problem using modular neural networks. In the application we use two interval type-2 fuzzy inference systems (IT2-FIS); the first IT2-FIS was used for feature extraction in the training data, and the second one to estimate the relevance of the modules in the multi-net system. Fuzzy logic is shown to be a tool that can help improve the results of a neural system by facilitating the representation of human perceptions.     

Focusing and polarization in linear,intuitionistic, and classical logics

A focused proof system provides a normal form to cut-free proofs in which the application of invertible and non-invertible inference rules is structured. Within linear logic, the focused proof system of Andreoli provides an elegant and comprehensive normal form for cut-free proofs. Within intuitionistic and classical logics, there are various different proof systems in the literature that exhibit focusing behavior. These focused proof systems have been applied to both the proof search and the proof normalization approaches to computation. We present a new, focused proof system for intuitionistic logic, called LJF, and show how other intuitionistic proof systems can be mapped into the new system by inserting logical connectives that prematurely stop focusing. We also use LJF to design a focused proof system LKF for classical logic. Our approach to the design and analysis of these systems is based on the completeness of focusing in linear logic and on the notion of polarity that appears in Girard’s LC and LU proof systems.     

Introduction: from legal theories to neural networks and fuzzy reasoning

Computational approaches to the law have frequently been characterized as being formalistic implementations of the syllogistic model of legal cognition: using insufficient or contradictory data, making analogies, learning through examples and experiences, applying vague and imprecise standards. We argue that, on the contrary, studies on neural networks and fuzzy reasoning show how AI & law research can go beyond syllogism, and, in doing that, can provide substantial contributions to the law.     

Structural synthesis of fast two-layer neural networks

Methods of construction of structural models of fast two-layer neural networks are considered. The methods are based on the criteria of minimum computing operations and maximum degrees of freedom. Optimal structural models of two-layer neural networks are constructed. Illustrative examples are given. Translated from Kibernetika i Sistemnyi Analiz, No. 4, pp. 47–56, July–August, 2000.     

High-level reasoning,computational challenges for connectionism,and the Conposit solution

Sophisticated symbol processing in connectionist systems can be supported by two primitive representational techniques calledRelative-Position Encoding (RPE) andPattern-Similarity Association (PSA), and a selection technique calledTemporal-Winner-Take-All (TWTA). TWTA effects winner-take-all selection on the basis of fine signal-timing differences as opposed to activation-level differences. Both RPE and PSA are for the encoding of highly temporary associations between representations. RPE is based on the way activation patterns are positioned relative to each other within a network. Under PSA, two patterns are temporarily associated if they have (suitable) subpatterns that are (suitably) similar. The article shows how particular versions of the primitives are used to good effect in a system called Conposit/SYLL. This is a connectionist implementation of a slightly simplified version of a complex existing psychological theory, namely Johnson-Laird's account of syllogistic reasoning. The computational processes in this theory present a major implementational challenge to connectionism. The challenge lies in the mutability, multiplicity, and diversity of the working memory structures, and the elaborateness of the processing needed for them. Conposit/SYLL's techniques allow it to meet the challenge. The implementation of symbolic processing in Conposit/SYLL is an interesting application of connectionism partly because it significantly affects the design of the symbolic processing level itself. In particular, it encourages the use of associative as opposed to pointer-based data structures, and the use of random as opposed to ordered iteration over sets of data structures. In addition, the article discusses Conposit/SYLL's somewhat unusual variable-binding approach.     

基于联合卷积和递归神经网络的交通标志识别

提出了一种联合卷积和递归神经网络的深层网络结构,在卷积神经网络中引入了递归神经网络能学到的组合特征:原始图片先通过一级由k均值聚类学得滤波器的卷积神经网络,得到的结果再同时通过一级卷积和一级递归神经网络,最后得到的特征向量由Softmax分类器进行分类。实验结果表明:在第二级卷积和递归神经网络权重随机的情况下,该网络的识别率已经能够达到98.28%,跟其他网络结构相比,大大减少了训练时间,而且无需复杂的工程技巧。     

多输出神经元模型的多层前向神经网络及其应用

研究了一种新的多输出神经元模型.首先,给出这类模型的一般形式,并将该模型应用于多层前向神经网络;其次,给出了其学习算法,即递推最小二乘算法,最后通过几个模拟实验表明,采用多输出神经元模型的多层前向神经网络,具有结构简单,泛化能力强,收敛速度快,收敛精度高等特点,其性能远远优于激活函数可调模型的多层前向神经网络.     

基于多层卷积神经网络特征和双向长短时记忆单元的行为识别

鲁棒的视频行为识别由于其复杂性成为了一项极具挑战的任务. 如何有效提取鲁棒的时空特征成为解决问题的关键. 在本文中, 提出使用双向长短时记忆单元(Bi--LSTM)作为主要框架去捕获视频序列的双向时空特征. 首先, 为了增强特征表达, 使用多层的卷积神经网络特征代替传统的手工特征. 多层卷积特征融合了低层形状信息和高层语义信息, 能够捕获丰富的空间信息. 然后, 将提取到的卷积特征输入Bi--LSTM, Bi--LSTM包含两个不同方向的LSTM层. 前向层从前向后捕获视频演变, 后向层反方向建模视频演变. 最后两个方向的演变表达融合到Softmax中, 得到最后的分类结果. 在UCF101和HMDB51数据集上的实验结果显示本文的方法在行为识别上可以取得较好的性能.     

基于模糊神经网络的民航物流预测研究与仿真分析

为合理规划我国机场改扩建方案,针对目前民航业特点,从客运量的角度对民航物流预测进行研究,在综合分析影响客运量因素的基础上,提出了模糊对角回归神经网络滚动预测模型.此模型在前端网络处理层对不确定性因素进行模糊量化处理,对确定性因素进行归一化处理,有效地解决了模型输入量纲不一致的问题.通过实际数据的检验与内回归神经网络、外回归神经网络的预测结果相比较,证明应用此模型进行民航客运量预测有较高的预测精度.并在此基础上利用Visual Basic语言开发了民航物流预测仿真系统,对预测结果进行仿真验证,试验结果表明该仿真系统具有广阔的应用前景和推广价值.     

Nominal Reasoning Techniques in Coq: (Extended Abstract)

We explore an axiomatized nominal approach to variable binding in Coq, using an untyped lambda-calculus as our test case. In our nominal approach, alpha-equality of lambda terms coincides with Coq's built-in equality. Our axiomatization includes a nominal induction principle and functions for calculating free variables and substitution. These axioms are collected in a module signature and proved sound using locally nameless terms as the underlying representation. Our experience so far suggests that it is feasible to work from such axiomatized theories in Coq and that the nominal style of variable binding corresponds closely with paper proofs. We are currently working on proving the soundness of a primitive recursion combinator and developing a method of generating these axioms and their proof of soundness from a grammar describing the syntax of terms and binding.     

基于序列输入的神经网络模型算法及应用

为提高神经网络的逼近能力,提出一种基于序列输入的神经网络模型及算法。模型隐层为序列神经元,输出层为普通神经元。输入为多维离散序列,输出为普通实值向量。先将各维离散输入序列值按序逐点加权映射,再将这些映射结果加权聚合之后映射为隐层序列神经元的输出,最后计算网络输出。采用Levenberg-Marquardt算法设计了该模型学习算法。仿真结果表明,当输入节点和序列长度比较接近时,模型的逼近能力明显优于普通神经网络。     

Neurovision-based logic control of an experimental manufacturing plant using neural net le-net5 and automation Petri nets

In this paper, Petri nets and neural networks are used together in the development of an intelligent logic controller for an experimental manufacturing plant to provide the flexibility and intelligence required from this type of dynamic systems. In the experimental setup, among deformed and good parts to be processed, there are four different part types to be recognised and selected. To distinguish the correct part types, a convolutional neural net le-net5 based on-line image recognition system is established. Then, the necessary information to be used within the logic control system is produced by this on-line image recognition system. Using the information about the correct part types and Automation Petri nets, a logic control system is designed. To convert the resulting Automation Petri net model of the controller into the related ladder logic diagram (LLD), the token passing logic (TPL) method is used. Finally, the implementation of the control logic as an LDD for the real time control of the manufacturing system is accomplished by using a commercial programmable logic controller (PLC).     

多层前向小世界神经网络及其函数逼近

借鉴复杂网络的研究成果, 探讨一种在结构上处于规则和随机连接型神经网络之间的网络模型—-多层前向小世界神经网络. 首先对多层前向规则神经网络中的连接依重连概率p进行重连, 构建新的网络模型, 对其特征参数的分析表明, 当0 < p < 1时, 该网络在聚类系数上不同于Watts-Strogatz 模型; 其次用六元组模型对网络进行描述; 最后, 将不同p值下的小世界神经网络用于函数逼近, 仿真结果表明, 当p = 0:1时, 网络具有最优的逼近性能, 收敛性能对比试验也表明, 此时网络在收敛性能、逼近速度等指标上要优于同规模的规则网络和随机网络.     

Application of artificial neural network and fuzzy logic in a case-based system for initial process parameter setting of injection molding

Determination of initial process meters for injection molding is a highly skilled job and based on skilled operators know-how and intuitive sense acquired through long-term experience rather than a theoretical and analytical approach. Facing with the global competition, the current trial-and-error practice becomes inadequate. In this paper, application of artificial neural network and fuzzy logic in a case-based system for initial process meter setting of injection molding is described. Artificial neural network was introduced in the case adaptation while fuzzy logic was employed in the case indexing and similarity analysis. A computer-aided system for the determination of initial process meter setting for injection molding based on the proposed techniques was developed and validated in a simulation environment. The preliminary validation tests of the system have indicated that the system can determine a set of initial process meters for injection molding quickly without relying on experienced molding personnel, from which good quality molded parts can be produced.     

Rule–based reasoning and neural network perception for safe off–road robot mobility

Operational safety and health monitoring are critical matters for autonomous field mobile robots such as planetary rovers operating on challenging terrain. This paper describes relevant rover safety and health issues and presents an approach to maintaining vehicle safety in a mobility and navigation context. The proposed rover safety module is composed of two distinct components: safe attitude (pitch and roll) management and safe traction management. Fuzzy logic approaches to reasoning about safe attitude and traction management are presented, wherein inertial sensing of safety status and vision–based neural network perception of terrain quality are used to infer safe speeds of traversal. Results of initial field tests and laboratory experiments are also described. The approach provides an intrinsic safety cognizance and a capacity for reactive mitigation of robot mobility and navigation risks.     

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.     

基于遗传算法学习的复合神经网络自适应温度控制系统

针对一类温度控制系统中存在的非线性和参数不确定等问题,提出一种复合神经网络自适应控制结构.在控制系统中构造了神经网络正模型来再现被控对象的动态特性,用神经网络控制器实现优化控制律的非线性映射.文中选用了被控对象80组历史数据作为样本集,并利用遗传算法的全局搜索能力及高效率来训练多层前向神经网络的权系数.最后用升降温工艺曲线作为输入对温度控制系统进行仿真.仿真结果表明,应用遗传算法能够提高神经网络的学习效率.保证神经网络全局快速收敛,从而克服了传统的误差反传学习算法的一些缺点.证明了采用这种神经网络自适应控制结构.使神经网络控制器的输出可以适应对象参数和环境的变化.使温度控制系统具有很好的学习和自适应控制能力,取得了良好的控制效果.     

Classical and superposed learning for quantum weightless neural networks

A supervised learning algorithm for quantum neural networks (QNN) based on a novel quantum neuron node implemented as a very simple quantum circuit is proposed and investigated. In contrast to the QNN published in the literature, the proposed model can perform both quantum learning and simulate the classical models. This is partly due to the neural model used elsewhere which has weights and non-linear activations functions. Here a quantum weightless neural network model is proposed as a quantisation of the classical weightless neural networks (WNN). The theoretical and practical results on WNN can be inherited by these quantum weightless neural networks (qWNN). In the quantum learning algorithm proposed here patterns of the training set are presented concurrently in superposition. This superposition-based learning algorithm (SLA) has computational cost polynomial on the number of patterns in the training set.