电子电路模糊神经网络故障诊断研究及仿真

针对模拟电路的故障特点,在确定智能诊断算法的基础上,论文尝试用高级语言编程来仿真实现.论文主要讨论了故障诊断的神经网络方法和将输入模糊化后的模糊神经网络方法,该方法结合了模糊逻辑和神经网络的优缺点,对于电阻元件的软故障,用模糊神经网络得到了比较理想的结果,对于所选的电路,用模糊神经网络可以对电阻增大和减小进行识别,实现了模拟电路软故障的辨识.对于模糊规则分不开的故障,可以运用神经网络进一步细分.论文着重以具体的模拟电路为研究对象编程模拟了诊断方法,结果证明这样的诊断方法是有效的.     

An 0.5-μm CMOS analog random access memory chip for TeraOPSspeed multimedia video processing

Data compressing, data coding, and communications in object-oriented multimedia applications like telepresence, computer-aided medical diagnosis, or telesurgery require an enormous computing power-in the order of trillions of operations per second (TeraOPS). Compared with conventional digital technology, cellular neural/nonlinear network (CNN)-based computing is capable of realizing these TeraOPS-range image processing tasks in a cost-effective implementation. To exploit the computing power of the CNN Universal Machine (CNN-UM), the CNN chipset architecture has been developed-a mixed-signal hardware platform for CNN-based image processing. One of the nonstandard components of the chipset is the cache memory of the analog array processor, the analog random access memory (ARAM). This paper reports on an ARAM chip that has been designed and fabricated in a 0.5-μm CMOS technology. This chip consists of a fully addressable array of 32×256 analog memory registers and has a packing density of 637 analog-memory-cells/mm². Random and nondestructive access of the memory contents is available. Bottom-plate sampling techniques have been employed to eliminate harmonic distortion introduced by signal-dependent feedthrough. Signal coupling and interaction have been minimized by proper layout measures, including the use of protection rings and separate power supplies for the analog and the digital circuitry. This prototype features an equivalent resolution of up to 7 bits-measured by comparing the reconstructed waveform with the original input signal. Measured access times for writing/reading to/from the memory registers are of 200 ns. I/O rates via the l6-line-wide I/O bus exceed 10 Msamples/s. Storage time at room temperature is in the 80 to 100 ms range, without accuracy loss     

Neural and Super-Turing Computing

``Neural computing' is a research field based on perceiving the human brain as an information system. This system reads its input continuously via the different senses, encodes data into various biophysical variables such as membrane potentials or neural firing rates, stores information using different kinds of memories (e.g., short-term memory, long-term memory, associative memory), performs some operations called ``computation', and outputs onto various channels, including motor control commands, decisions, thoughts, and feelings. We show a natural model of neural computing that gives rise to hyper-computation. Rigorous mathematical analysis is applied, explicating our model's exact computational power and how it changes with the change of parameters. Our analog neural network allows for supra-Turing power while keeping track of computational constraints, and thus embeds a possible answer to the superiority of the biological intelligence within the framework of classical computer science. We further propose it as standard in the field of analog computation, functioning in a role similar to that of the universal Turing machine in digital computation. In particular an analog of the Church-Turing thesis of digital computation is stated where the neural network takes place of the Turing machine.     

Bankruptcy prediction with neural logic networks by means of grammar-guided genetic programming

The paper demonstrates the efficient use of hybrid intelligent systems for solving the classification problem of bankruptcy. The aim of the study is to obtain classification schemes able to predict business failure. Previous attempts to form efficient classifiers for the same problem using intelligent or statistical techniques are discussed throughout the paper. The application of neural logic networks by means of genetic programming is proposed. This is an advantageous approach enabling the interpretation of the network structure through set of expert rules, which is a desirable feature for field experts. These evolutionary neural logic networks are consisted of an innovative hybrid intelligent methodology, by which evolutionary programming techniques are used for obtaining the best possible topology of a neural logic network. The genetic programming process is guided using a context-free grammar and indirect encoding of the neural logic networks into the genetic programming individuals. Indicative classification results are presented and discussed in detail in terms of both, classification accuracy and solution interpretability.     

神经元网络控制系统

人工神经元网络基于其特有的功能而被应用于控制系统中,为智能控制提供了一个有潜力的发展方向,本文讨论了神经元网络控制的三种方法:基于模式识别功能的BP网络控制器,基于联想记忆模型的模糊控制,以及基于神经元网络优化功能的控制优化调节,并分别指出其优、缺点及有待解决的一些问题。     

Neural networks applications in concrete structures

This paper presents and discusses the applications of neural networks in concrete structures. It aims at introducing neural networks applications in structural design. The paper covers two applications of neural networks in concrete structures. Backpropagation networks are chosen for the proposed network, which is written using the programming package MAT-LAB. The overall results are compared and observed for the performance of the networks. Based on the applications it was found that neural networks are comparatively effective for a number of reasons, which include the amount of CPU memory consumed by neural networks is less than that consumed by conventional methods and their ease of use and implementation, neural networks provide both the users and the developers more flexibility to cope with different kinds of problems.     

模拟神经网络VLSI脉冲流技术在故障诊断中的应用分析

介绍了模拟神经网络VLSI脉冲流技术实现神经网络模式识别硬件电路的方法，并且直接将故障分类。提出利用包含有故障信息的原始模拟噪声信号，经过前置信号处理和神经网络运算，得出VLSI电路输出端电容的电压值－代表待识别信号与模板故障信号的“欧氏距离”，以实现噪声故障信号的实时硬件在线识别。     

Lagrange programming neural networks for time-of-arrival-based source localization

Finding the location of a mobile source from a number of separated sensors is an important problem in global positioning systems and wireless sensor networks. This problem can be achieved by making use of the time-of-arrival (TOA) measurements. However, solving this problem is not a trivial task because the TOA measurements have nonlinear relationships with the source location. This paper adopts an analog neural network technique, namely Lagrange programming neural network, to locate a mobile source. We also investigate the stability of the proposed neural model. Simulation results demonstrate that the mean-square error performance of our devised location estimator approaches the Cramér–Rao lower bound in the presence of uncorrelated Gaussian measurement noise.     

Assessment of artificial neural network and genetic programming as predictive tools

Soft computing techniques have been widely used during the last two decades for nonlinear system modeling, specifically as predictive tools. In this study, the performances of two well-known soft computing predictive techniques, artificial neural network (ANN) and genetic programming (GP), are evaluated based on several criteria, including over-fitting potential. A case study in punching shear prediction of RC slabs is modeled here using a hybrid ANN (which includes simulated annealing and multi-layer perception) and an established GP variant called gene expression programming. The ANN and GP results are compared to values determined from several design codes. For more verification, external validation and parametric studies were also conducted. The results of this study indicate that model acceptance criteria should include engineering analysis from parametric studies.     

A time-multiplexed reconfigurable neuroprocessor

Built around biological-like information-processing structures, artificial neural networks (ANNs) have demonstrated their power and usefulness in areas where identification and adaptability are most crucial. After sufficient training with a given set of problem data (using an arbitrary learning rule), ANNs can independently form internal representations (models) of the data's underlying phenomenon. ANNs typically have a large number of highly interconnected processing elements, which constrains their hardware implementation and network architecture. To obtain high density and processing-element connectivity, most ANN architectures employ some kind of resource sharing. A multilayer structure allows processing elements to share communication lines and control circuitry, so we chose to develop a multilayered neuroprocessor based on pipelined time-step interneural communication. Other pulse stream architectures use the rate of impulses to represent the neural states. Our architecture, however, represents the neural states through pulse amplitude modulation. Also, in our design, the analog computation consists of integrating the bipolar input pulses corresponding to the excitatory and inhibitory activations. The processing-element analog path consists. of CMOS transmission gates controlled by buffered signals originating from the neuroprocessor control unit. The processing elements broadcast their output states, held on a local capacitor, during their assigned time slots. These features are desirable to meet the design goals of versatility, high density, high connectivity, and scalability     

Programming based learning algorithms of neural networks withself-feedback connections

Discusses the learning problem of neural networks with self-feedback connections and shows that when the neural network is used as associative memory, the learning problem can be transformed into some sort of programming (optimization) problem. Thus, the rather mature optimization technique in programming mathematics can be used for solving the learning problem of neural networks with self-feedback connections. Two learning algorithms based on programming technique are presented. Their complexity is just polynomial. Then, the optimization of the radius of attraction of the training samples is discussed using quadratic programming techniques and the corresponding algorithm is given. Finally, the comparison is made between the given learning algorithm and some other known algorithms     

Neural network for solving extended linear programming problems.

A neural network for solving extended linear programming problems is presented and is shown to be globally convergent to exact solutions. The proposed neural network only uses simple hardware in which no analog multiplier for variables is required, and has no parameter tuning problem. Finally, an application of the neural network to the L(1 )-norm minimization problem is given.     

Evolving a modular neural network-based behavioral fusion usingextended VFF and environment classification for mobile robot navigation

A local navigation algorithm for mobile robots is proposed that combines rule-based and neural network approaches. First, the extended virtual force field (EVFF), an extension of the conventional virtual force field (VFF), implements a rule base under the potential field concept. Second, the neural network performs fusion of the three primitive behaviors generated by EVFF. Finally, evolutionary programming is used to optimize the weights of the neural network with an arbitrary form of objective function. Furthermore, a multinetwork version of the fusion neural network has been proposed that lends itself to not only an efficient architecture but also a greatly enhanced generalization capability. Herein, the global path environment has been classified into a number of basic local path environments to which each module has been optimized with higher resolution and better generalization. These techniques have been verified through computer simulation under a collection of complex and varying environments     

基于忆阻器的PIM结构实现深度卷积神经网络近似计算

忆阻器(memristor)能够将存储和计算的特性融合,可用于构建存储计算一体化的PIM(processing-in-memory)结构.但是,由于计算阵列以及结构映射方法的限制,基于忆阻器阵列的深度神经网络计算需要频繁的AD/DA转换以及大量的中间存储,导致了显著的能量和面积开销.提出了一种新型的基于忆阻器的深度卷积神经网络近似计算PIM结构,利用模拟忆阻器大大增加数据密度,并将卷积过程分解到不同形式的忆阻器阵列中分别计算,增加了数据并行性,减少了数据转换次数并消除了中间存储,从而实现了加速和节能.针对该结构中可能存在的精度损失,给出了相应的优化策略.对不同规模和深度的神经网络计算进行仿真实验评估,结果表明,在相同计算精度下,该结构可以最多降低90%以上的能耗,同时计算性能提升约90%.     

基于神经网络的系统建模研究

从基本的神经网络结构出发,构建了一个多输入单输出的神经网络系统,使用这个模型对电容柜各组数据的最大值进行预测,取得了良好的效果。继而改进上述神经网络模型,加入循环迭代操作,得到了动态的预测效果。从Matlab仿真结果中看到,对最大温度和将来温度的预测都有良好的效果,为进一步做故障诊断、预测控制打下了基础,使用Matlab编程来实现软件的仿真为将来进一步做软件的移植和通用做好了准备。     

自反馈神经网络的椭球学习算法

本文讨论自反馈神经网络的学习问题，指出联想记忆的神经网络的学习可以化为某种规划（优化）的问题来解，于是可借用规划数学中发展得成熟的优化技术来解自反馈神经网络的学习问题，文中给出一种称为椭球算法的学习方法，其计算复杂性是多项式型。     

A review on weight initialization strategies for neural networks

Over the past few years, neural networks have exhibited remarkable results for various applications in machine learning and computer vision. Weight initialization is a significant step employed before training any neural network. The weights of a network are initialized and then adjusted repeatedly while training the network. This is done till the loss converges to a minimum value and an ideal weight matrix is obtained. Thus weight initialization directly drives the convergence of a network. Therefore, the selection of an appropriate weight initialization scheme becomes necessary for end-to-end training. An appropriate technique initializes the weights such that the training of the network is accelerated and the performance is improved. This paper discusses various advances in weight initialization for neural networks. The weight initialization techniques in the literature adopted for feed-forward neural network, convolutional neural network, recurrent neural network and long short term memory network have been discussed in this paper. These techniques are classified as (1) initialization techniques without pre-training, which are further classified into random initialization and data-driven initialization, (2) initialization techniques with pre-training. The different weight initialization and weight optimization techniques which select optimal weights for non-iterative training mechanism have also been discussed. We provide a close overview of different initialization schemes in these categories. This paper concludes with discussions on existing schemes and the future scope for research.

     

A neural network approach to job-shop scheduling

A novel analog computational network is presented for solving NP-complete constraint satisfaction problems, i.e. job-shop scheduling. In contrast to most neural approaches to combinatorial optimization based on quadratic energy cost function, the authors propose to use linear cost functions. As a result, the network complexity (number of neurons and the number of resistive interconnections) grows only linearly with problem size, and large-scale implementations become possible. The proposed approach is related to the linear programming network described by D.W. Tank and J.J. Hopfield (1985), which also uses a linear cost function for a simple optimization problem. It is shown how to map a difficult constraint-satisfaction problem onto a simple neural net in which the number of neural processors equals the number of subjobs (operations) and the number of interconnections grows linearly with the total number of operations. Simulations show that the authors' approach produces better solutions than existing neural approaches to job-shop scheduling, i.e. the traveling salesman problem-type Hopfield approach and integer linear programming approach of J.P.S. Foo and Y. Takefuji (1988), in terms of the quality of the solution and the network complexity.     

深度学习在多核缓存预取中的应用研究综述

当前人工智能技术应用于系统结构领域的研究前景广阔,特别是将深度学习应用于多核架构的数据预取研究已经成为国内外的研究热点。针对基于深度学习的缓存预取任务进行了研究,形式化地定义了深度学习缓存预取模型。在介绍当前常见的多核缓存架构和预取技术的基础上,全面分析了现有基于深度学习的典型缓存预取器的设计思路。深度学习神经网络在多核缓存预取领域的应用主要采用了深度神经网络、循环神经网络、长短期记忆网络和注意力机制等机器学习方法,综合对比分析现有基于深度学习的数据预取神经网络模型后发现,基于深度学习的多核缓存预取技术在计算成本、模型优化和实用性等方面还存在着局限性,未来在自适应预取模型以及神经网络预取模型的实用性方面还有很大的研究探索空间和发展前景。     

A new neural network for solving linear programming problems andits application

Presents a new neural network which improves existing neural networks for solving general linear programming problems. The network, without setting parameter, uses only simple hardware in which no analog multipliers are required, and is proved to be completely stable to the exact solutions. Moreover, using this network the author can solve linear programming problems and its dual simultaneously, and cope with problems with nonunique solutions whose set is allowed to be unbounded.