Modeling deterministic echo state network with loop reservoir

Echo state network (ESN),which efficiently models nonlinear dynamic systems,has been proposed as a special form of recurrent neural network.However,most of the proposed ESNs consist of complex reservoi...     

Predicting dam failure risk for sustainable flood retention basins: A generic case study for the wider Greater Manchester area

This study aims to provide a rapid screening tool for assessment of sustainable flood retention basins (SFRBs) to predict corresponding dam failure risks. A rapid expert-based assessment method for dam failure of SFRB supported by an artificial neural network (ANN) model has been presented. Flood storage was assessed for 110 SFRB and the corresponding Dam Failure Risk was evaluated for all dams across the wider Greater Manchester study area. The results show that Dam Failure Risk can be estimated by using the variables Dam Height, Dam Length, Maximum Flood Water Volume, Flood Water Surface Area, Mean Annual Rainfall (based on Met Office data), Altitude, Catchment Size, Urban Catchment Proportion, Forest Catchment Proportion and Managed Maximum Flood Water Volume. A cross-validation R² value of 0.70 for the ANN model signifies that the tool is likely to predict variables well for new data sets. Traditionally, dams are considered safe because they have been built according to high technical standards. However, many dams that were constructed decades ago do not meet the current state-of-the-art dam design guidelines. Spatial distribution maps show that dam failure risks of SFRB located near cities are higher than those situated in rural locations. The proposed tool could be used as an early warning system in times of heavy rainfall.     

VPN构建的一种设计思想

Internet的发展对安全问题的要求越来越高，网络安全日益受到重视。文章以虚拟专用网为主要研究对象，结合软件工程的一些开发思想，对虚拟专用网的构建做出了理论上的设计。文章分析了虚拟专用网目前所涉及的主要几大模型，具体构建了一种可行的VPN设计方案，同时结合软件工程，对VPN构建给出自行设计的一种模块结构图，并对各个模块结构给出了具体解释，今后的工作在于将模块具体细化运用到实践中。     

基于神经元网络的燃油锅炉烟气氧含量的软

针对目前各种氧含量检测仪表在运行过程中的可靠性差、使用寿命短及价格昂贵等诸多不足，通过探寻锅炉运行中的其他参数与烟气氧含量的映射关系，建立ＢＰ神经元网络，提出了燃油锅炉烟气氧含量的软测量方法。结果表明，其精度完全达到了设计要求。     

神经网络控制器用于永磁电机的伺服驱动

提出用一种遗传算法（ＧｅｎｅｔｉｃＡｌｇｏｒｉｔｈｍ，简称ＧＡ）训练的多层神经网络结构（ＮＮ），并把它用于交流直线永磁同步电动机（简称ＬＰＭＳＭ）的辩识与控制，使其运行在伺服驱动环境．ＮＮ可随时辩识被控系统的动态待性，而不需要ＬＰＭＳＭ的精确的数学模型．仿真结果令人满意     

一类时滞神经网络系统的局部稳定性与分枝

给出一类时滞神经网络系统的零解的局部稳定判定准则，并给出该系统出现Ｈｏｐｆ分枝的参数条件     

Modeling users through an expert system and a neural network

With the number of Internet and Web users increasing rapidly, electronic service providers are competing to satisfy and better serve customers looking for information or channels of advertisement. A wide variety of browses, specialized sites, custom made software, etc. are being offered on a regular basis. However, the user has to filter through a large number of files before finding what he/she is really looking for. This paper presents a user modeling expert system, SIGMA, based on neural networks for encapsulating Internet and Web users' habits and preferences. SIGMA is an artificial intelligence application designed to answer an Internet client needs and preferences. It analyses the user supplied demographic data and the monitored transactions then generate a tailored profile that is ultimately used to filter what information is being passed on to him/her in an effort to reduce and hopefully eliminate the time and energy expended in sifting through raw and often unwanted data.     

Simulation based on learning methods

The use of simulation technology as a tool for planning and control is of increasing significance in most fields of production. The main part of the expenditure concerning simulation analyses is the modelling of the considered production. Despite the use of modern building-block-oriented modelling technology, this modelling can often not be done by the user, but only by external experts. Against this backdrop, an adaptive simulation system is being developed by the Institute for Industrial Manufacturing and Management (IFF) at the University of Stuttgart. It independently adapts to real production processes, i.e. it learns about the interdependencies of production processes, and, in this way, supports the user in constructing and maintaining the model. In terms of information technology, the research in the field of artificial intelligence, especially in the subdomain of machine learning, is the basis for the realization of such adaptive systems.     

Colour classification by neural networks in graphic arts

This paper presents a hierarchical modular neural network for colour classification in graphic arts, capable of distinguishing among very similar colour classes. The network performs analysis in a rough to fine fashion, and is able to achieve a high average classification speed and a low classification error. In the rough stage of the analysis, clusters of highly overlapping colour classes are detected. Discrimination between such colour classes is performed in the next stage by using additional colour information from the surroundings of the pixel being classified. Committees of networks make decisions in the next stage. Outputs of members of the committees are adaptively fused through the BADD defuzzification strategy or the discrete Choquet fuzzy integral. The structure of the network is automatically established during the training process. Experimental investigations show the capability of the network to distinguish among very similar colour classes that can occur in multicoloured printed pictures. The classification accuracy obtained is sufficient for the network to be used for inspecting the quality of multicoloured prints.     

A Scalable Interconnection Network Architecture for Petaflops Computing

Extrapolating technology advances in the near future, a computer architecture capable of petaflops performance will likely be based on a collection of processing nodes interconnected by a high-performance network. One possible organization would consist of thousands of inexpensive, low-power symmetric multiprocessor (SMP) nodes. Each node will inject data into the interconnection network at a very large rate and consequently, the interconnect scheme is one of the most crucial design issues affecting system performance. This paper describes the 2D simultaneous optical multiprocessor exchange bus (2D SOME-Bus) which has the potential to become the basis of a high-end computer architecture capable of petaflops performance. It consists of N horizontal, N vertical 1D SOME-Bus networks, and N ² nodes. Each node is connected to one horizontal and one vertical 1D SOME-Bus. Each of N nodes connected to one 1D SOME-Bus has a dedicated broadcast channel and an input channel interface based on an array of N receivers monitoring all N channels and allowing multiple simultaneous broadcasts. In the 2D SOME-Bus, messages being broadcast on one Bus can be broadcast in a cut-through manner on one or more Buses in the other dimension. This paper describes the optoelectronic devices and technology which make the 2D SOME-Bus possible, and the network interface organization. It also presents simulation results which compare the performance of the 2D SOME-Bus, the 1D SOME-Bus, the crossbar and the torus under the message-passing paradigm.