基于UKF的自适应DFE信道均衡

针对信道的线性和非线性失真,在分析简化的非线性滤波的基础上,利用判决反馈的结构特点对其进行扩展,提出了基于UKF滤波的判决反馈均衡器,仿真表明,UKF滤波算法能降低系统均方误差性能。     

On the stability structure for lattice Boltzmann schemes

The stability structure for lattice Boltzmann schemes has been introduced in Banda et al. (2006) [16], Junk and Yong (2007) [14] to analyze the stability of numerical algorithms. The first purpose of this paper is to discuss the stability structure from the perspective of matrix analysis. Its second goal is to illustrate and apply the results to different classes of lattice Boltzmann collision operators. In particular we formulate an equivalence condition–just recently also reported in Yong (2008) [18]–that guarantees the existence of a pre-stability structure. It is then illustrated by several examples, how this equivalence condition can be effectively employed for the systematic verification and construction of stable collision operators. Finally, we point out some shortcomings of the stability structure approach arising in certain cases.     

Multilayer discrete-time neural-net controller with guaranteedperformance

A family of novel multilayer discrete-time neural-net (NN) controllers is presented for the control of a class of multi-input multi-output (MIMO) dynamical systems. The neural net controller includes modified delta rule weight tuning and exhibits a learning while-functioning-features. The structure of the NN controller is derived using a filtered error/passivity approach. Linearity in the parameters is not required and certainty equivalence is not used. This overcomes several limitations of standard adaptive control. The notion of persistency of excitation (PE) for multilayer NN is defined and explored. New online improved tuning algorithms for discrete-time systems are derived, which are similar to sigma or epsilon-modification for the case of continuous-time systems, that include a modification to the learning rate parameter plus a correction term. These algorithms guarantee tracking as well as bounded NN weights in nonideal situations so that PE is not needed. An extension of these novel weight tuning updates to NN with an arbitrary number of hidden layers is discussed. The notions of discrete-time passive NN, dissipative NN, and robust NN are introduced. The NN makes the closed-loop system passive.     

A framework for integrating information sources under lattice structure

Different observations over one and the same natural phenomenon often lead to different collections of information elements describing that phenomenon. Such collections of information elements, which we call information sources, can be heterogeneous, redundant, complementary, or even contradictory. In general, the information elements describing the phenomenon can be unified and related in a hierarchical structure, using existing methods for data integration and ontology merging. In this paper, we consider the problem of redundancy, complementarity, and consistency of information sources, under the assumption that the information elements of each source are related in a lattice structure. We propose various methods for integrating information sources and establish relationships between these methods. Applications of the framework are illustrated through examples in the areas of geographic information and battlefield target identification.     

Prediction of interfacial reaction products between metals with same lattice structure through thermodynamic modeling

Interfacial reactions exert crucial effect on the performance of soldering joints. Prediction of the first phase formed at the interface between metals benefit the design of electronic packaging. Based on our previous work, a new scheme is developed in this paper, which aims at predicting phase formation of the interface reaction between metals with the same lattice structure. In the new scheme, the conventional principle considering both the thermodynamic and kinetic factors is employed. A criterion is introduced for the case where a spinodal decomposition exists in the starting solution when the local equilibrium assumption is still valid. Another new criterion involving only nucleation driving force and entropy change is proposed for the case where no spinodal decomposition occurs and the assumption of local equilibrium is no longer valid. The validity of these criteria has been confirmed when applied to predict the intermetallic compound (IMC) formed in the binary diffusion couples such as Al/Cu, Al/Pt, Al/Au, and Co/Zn.     

A multi-agent approach using perceptron-based learning for robust operation of distributed chemical reactor networks

Controlling the individual reactors of a chemical reactor network producing different grades of a product requires intelligent reconfiguration strategies. Agent-based approaches are ideal for such distributed manufacturing processes, since they provide flexible, robust, and emergent solutions under dynamically changing process conditions. This paper proposes a multi-layered, multi-agent framework based on a decentralized online learning approach for the supervision of grade transitions in autocatalytic reactor networks. The values for the manipulated variables and the path to the target reactor are determined to give the least disturbance to the system. Case studies illustrate the performance of the approach in managing grade transition and disturbance rejection in a reactor network.     

Multilayer feedforward networks with adaptive spline activationfunction

In this paper, a new adaptive spline activation function neural network (ASNN) is presented. Due to the ASNN's high representation capabilities, networks with a small number of interconnections can be trained to solve both pattern recognition and data processing real-time problems. The main idea is to use a Catmull-Rom cubic spline as the neuron's activation function, which ensures a simple structure suitable for both software and hardware implementation. Experimental results demonstrate improvements in terms of generalization capability and of learning speed in both pattern recognition and data processing tasks.     

Multilayer neural-net robot controller with guaranteed trackingperformance

A multilayer neural-net (NN) controller for a general serial-link rigid robot arm is developed. The structure of the NN controller is derived using a filtered error/passivity approach. No off-line learning phase is needed for the proposed NN controller and the weights are easily initialized. The nonlinear nature of the NN, plus NN functional reconstruction inaccuracies and robot disturbances, mean that the standard delta rule using backpropagation tuning does not suffice for closed-loop dynamic control. Novel online weight tuning algorithms, including correction terms to the delta rule plus an added robust signal, guarantee bounded tracking errors as well as bounded NN weights. Specific bounds are determined, and the tracking error bound can be made arbitrarily small by increasing a certain feedback gain. The correction terms involve a second-order forward-propagated wave in the backpropagation network. New NN properties including the notions of a passive NN, a dissipative NN, and a robust NN are introduced.     

The Galois lattice as a hierarchical structure for topological relations

This paper presents the construction and the comparison of Galois lattices of topological relations for qualitative spatial representation and reasoning. The lattices rely on a correspondence between computational operations working on quantitative data, on the one hand, and topological relations working on qualitative knowledge units, on the other hand. After introducing the context of the present research work, i.e. the RCC-8 model of topological relations, we present computational operations for checking topological relations on spatial regions. From these operations are derived two sets of computational conditions that can be associated to topological relations through a Galois connection. The associated Galois lattices are presented and compared. Elements on the practical use of the lattices for representing spatial knowledge and for reasoning are also introduced and discussed.     

短波多径衰落信道下的判决反馈均衡器研究

判决反馈均衡器(DFE)主要有两种基本类型:直接调整抽头系数的传统DFE和通过估计信道冲激响应直接计算均衡器抽头系数的基于信道估计DFE.研究两种基本的DFE和一种改进的基于信道估计DFE在短波多径衰落信道下的性能.仿真结果显示,基于信道估计DFE尤其是改进的基于信道估计DFE,在对抗信道的时变性方面与传统DFE相比,具有更强的鲁棒性,更适合在短波通信中使用.     

DFE: efficient IoT network intrusion detection using deep feature extraction

Neural Computing and Applications - In recent years, the Internet of Things (IoT) has received a lot of attention. It has been used in many applications such as the control industry, industrial...     

多线激光雷达越野环境障碍检测

运用多线激光雷达实现了越野环境中障碍物的检测.为了分析了传感器安装参数对系统性能的影响,采用设计了分层聚类算法,实现了场景中数据的分类;对影响雷达测距精度的因素进行了分析,给出了雷达数据的滤波方法;以相对高度,坡度和点密度作为判决条件,完成了越野环境中障碍物的识别.算法应用到了越野环境下无人驾驶车辆的导航上,试验证明方法稳定可靠,符合车辆自主行驶的要求.     

Evolving Multilayer Perceptrons

This paper proposes a new version of a method (G-Prop, genetic backpropagation) that attempts to solve the problem of finding appropriate initial weights and learning parameters for a single hidden layer Multilayer Perceptron (MLP) by combining an evolutionary algorithm (EA) and backpropagation (BP). The EA selects the MLP initial weights, the learning rate and changes the number of neurons in the hidden layer through the application of specific genetic operators, one of which is BP training. The EA works on the initial weights and structure of the MLP, which is then trained using QuickProp; thus G-Prop combines the advantages of the global search performed by the EA over the MLP parameter space and the local search of the BP algorithm. The application of the G-Prop algorithm to several real-world and benchmark problems shows that MLPs evolved using G-Prop are smaller and achieve a higher level of generalization than other perceptron training algorithms, such as QuickPropagation or RPROP, and other evolutive algorithms. It also shows some improvement over previous versions of the algorithm.     

DFE和MLSE相结合的自适应信道均衡技术及其FPGA实现

文章首先提出了一种新的均衡技术,即DFE插入MLSE的DFE/MLSE自适应均衡器结构,接着提出了如何用FPGA实现该均衡器。     

Design and simulate the properties of triangular periodic nanoparticle arrays with the structure of bi-layer hexagonal lattice

Microsystem Technologies - In this study, the discrete dipole approximation (DDA) method was used to simulate the hybrid bi-layer hexagonal lattice of triangular periodic nanoparticle arrays...     

Chain coding with a hexagonal lattice

This paper investigates the performance of chain code quantization of general curves using a hexagonal lattice structure, as a means of improving efficiency over the standard square lattice. Performance is first computed theoretically, assuming a generalization of grid-intersect quantization, and the curve to be quantized is assumed to be a straight line. An algorithm is then developed to perform chain coding using the hex lattice. Computer simulations were performed to evaluate hexagonal chain coding for a variety of curves, including circles of various curva-ture, straight lines, and a stochastic curve model. We find that the straight-line theory is substantiated for curves whose radius of curvature is roughly twice the lattice constant. For a given peak error in quanti-zation, hexagonal coding reduces the bit rate about 15 percent relative to the square lattice codes, and exhibits qualitative improvements in fidelity as well.     

Simulating thermohydrodynamics with lattice BGK models

In this paper, we propose new lattice BGK models for one-, two- and three-dimensional thermohydrodynamics. A proper internal energy is introduced and the energy equation is obtained. The derivation of the thermohydrodynamic equations is systematic and numerical simulations are carried out to verify the theoretical values of the sound speed, the shear viscosity and the conductivity. One-dimensional shock-tube problem and two-dimensional Rayleigh-Bénard convection are simulated. Good performance and satisfactory results are obtained. These models can be applied to many interesting cases, in particular, the transonic regimes where the compressibility can not be neglected.     

改进的蚁群算法及其在2DHP模型中的应用

为了提高蛋白质折叠问题解的质量,采用蚁群算法对蛋白质的折叠问题进行研究,并且在现有的蚁群算法的基础上成功引入了淘汰和克隆机制,使其具有更好的运算效率,并成功应用到2DHP模型中.在蚁群对最优值进行搜索的过程中,容易出现局部最优点,导致影响解的质量.为了避免计算结果收敛到局部最优点,引入了一种最大最小蚁群策略.选择测试序列进行实验,实验结果表明,该算法在保证解的质量的同时,还具有较高的效率.     

Multilayer perceptron with different training algorithms for streamflow forecasting

Streamflow forecasting has always been a challenging task for water resources engineers and managers. This study applies Multilayer Perceptron (MLP) networks optimized with three training algorithms, including resilient back-propagation (MLP_RP), variable learning rate (MLP_GDX), and Levenberg–Marquardt (MLP_LM), to forecast streamflow in Aspas Watershed, located in Fars province in southwestern Iran. The algorithms were trained and tested using 3 years of data. Antecedent streamflow with 1 day time lag constituted the first input vector, and MLP with this vector, labeled as MLP1 was the first model. Inclusion of streamflow with two, three, and four time lags led to input vectors 2, 3, and 4 which when combined with MLP resulted in MLP2, MLP3, and MLP4, respectively. It was found that the Levenberg–Marquardt algorithm performed best among three types of training algorithms employed for training the MLP models. Generally, the MLP4_LM model yields the best result with a determination coefficient and a root mean square error of 0.93 and 2.6 (m³/s).     

Genetic algorithm with advanced mechanisms applied to the protein structure prediction in a hydrophobic-polar model and cubic lattice

This paper presents a genetic algorithm applied to the protein structure prediction in a hydrophobic-polar model on a cubic lattice. The proposed genetic algorithm is extended with crowding, clustering, repair, local search and opposition-based mechanisms. The crowding is responsible for maintaining the good solutions to the end of the evolutionary process while the clustering is used to divide a whole population into a number of subpopulations that can locate different good solutions. The repair mechanism transforms infeasible solutions to feasible solutions that do not occupy the lattice point for more than one monomer. In order to improve convergence speed the algorithm uses local search. This mechanism improves the quality of conformations with the local movement of one or two consecutive monomers through the entire conformation. The opposition-based mechanism is introduced to transform conformations to the opposite direction. In this way the algorithm easily improves good solutions on both sides of the sequence. The proposed algorithm was tested on a number of well-known hydrophobic-polar sequences. The obtained results show that the mechanisms employed improve the algorithm's performance and that our algorithm is superior to other state-of-the-art evolutionary and swarm algorithms.