Stability of an analog optimization circuit for quadratic programming

We study the stability of an analog optimization circuit that solves quadratic programming (QP) problems. The circuit dynamics are modeled as a switched affine system. A piece-wise quadratic Lyapunov function and the KYP lemma are used to derive the stability criterion. The stability criterion characterizes the range of critical circuit parameters for which the QP circuit is globally exponentially stable.     

模拟电路单软故障的改进粒子群算法诊断

针对基本粒子群算法实现模拟电路单软故障诊断时存在容易收敛于局部最优值、搜索时间长的缺陷,提出一种改进粒子群算法,并应用于模拟电路的单软故障诊断。在以往文献的基础上,受生物学界理论研究成果的启发,引入领导机制,提出改进算法。通过灵敏度分析建立电路测试节点电压增量方程,从而建立起模拟电路故障诊断的约束线性规划方程组;引入罚函数将所建立的方程组转换为粒子搜索过程中的适应度函数,从而将改进算法应用于模拟电路单软故障诊断。实验结果证明,与基本粒子群算法相比较,改进后的粒子群算法搜索到的结果更接近于实际情况,搜索迭代     

一种模拟量输出接口的设计

介绍了D／A转换芯片DAC7625和电压／电流转换芯片XTR110的原理及应用，采用DAC7625和XTR110，设计了一种DSP系统的模拟量输出接口。     

基于二次优化的随机优化算法结果的改进

统计研究发现,随机优化算法多次运行后的优化结果满足正态分布,且期望值更接近最优解。为此,提出一种基于统计学理论并结合牛顿法的二次优化方法来改进随机优化算法的求解结果,以克服将多次优化结果的平均值作为最优解时不能满足精度要求的缺陷。以遗传算法对4个经典测试函数的多次优化为例,分别运用平均法和二次优化法来综合其优化结果。多次实验表明,二次优化法在处理多次随机运行结果时,比平均法精度更高、稳定性更好。     

On analog circuit design methodology via multi-objective geometric programming

This paper concerns a design methodology for analog circuit problems, which is based on formulating the problem as a multi-objective geometric program （GP）. Multiple objectives are combined into a scalar objective thru certain convex functions and thus can be solved using regular GP algorithms. In contrast to single-objective optimization, simultaneous consideration of all objectives yields a compromising solution among objectives. By varying individual objective＇s weight factor, a Pareto （or trade-off） curve can be constructed, providing designers with a useful decision-making information.     

基于PSO的负反馈电路参数自适应优化及仿真分析

采用粒子群优化算法,以电压增益、共模抑制比、输入电阻平方根的三者乘积对输出电阻的比作为适应度函数,对差分 共射两级直接耦合电压串联负反馈放大电路中的电阻做自适应优化。结果显示,只要对电路交流指标加以约束,适应度函数值总会减小。当分别对增大电压增益和减小输出电阻进行限制后,电压增益总是尽量小,输出电阻总是尽量大,以使适应度函数在给定约束下取得最大。经EWB软件对优化参数仿真,结果满足线性放大要求。同时说明了可以调整适应度函数形式,找到最佳电路参数,以满足工程上对放大器指标的不同需求。     

Improving optimization of tool path planning in 5-axis flank milling using advanced PSO algorithms

This paper studies optimization of tool path planning in 5-axis flank milling of ruled surfaces using advanced Particle Swarm Optimization (PSO) methods with machining error as an objective. We enlarge the solution space in the optimization by relaxing the constraint imposed by previous studies that the cutter must make contact with the boundary curves. Advanced Particle Swarm Optimization (APSO) and Fully Informed Particle Swarm Optimization (FIPS) algorithms are applied to improve the quality of optimal solutions and search efficiency. Test surfaces are constructed by systematic variations of three surface properties, cutter radius, and the number of cutter locations comprising a tool path. Test results show that FIPS is most effective in reducing the error in all the trials, while PSO performs best when the number of cutter locations is very low. This research improves tool path planning in 5-axis flank milling by producing smaller machining errors compared to past works. It also provides insightful findings in PSO based optimization of the tool path planning.     

A yield-enhanced global optimization methodology for analog circuit based on extreme value theory

The challenge in variation-aware circuit optimization with consideration of yield is the trade-off between optimized performance, yield and optimization runtime. This paper presents a practical variationaware circuit global optimization framework named GOYE, which shows the advantages on performance, yield and runtime. It uses an approach called constraint violation elimination (CVE) in global search phase to prune initial starting points and uses the gradient-based method in local search to locate optimum. The worst-case analysis (WCA), which is necessary for variation-aware circuit optimization, is nested in the local optimization process. The efficiency is significantly improved by a novel method based on extreme value theory (EVT). Our EVT-based method is also the first one that allows users to control the target yield such that under-design or over-design can be avoided. A design example in TSMC 65 nm technology is illustrated in the paper where all performance achieves three-sigma yield with consideration of environmental and inter-die/intra-die process variations.     

Watermarking via optimization algorithms for quantizing randomized semi-global image statistics

We introduce a novel approach for blind and semi-blind watermarking and apply it to images. We derive randomized robust semi-global statistics of images in a suitable transform domain (wavelets in case of images) and quantize them in order to embed the watermark. Quantization is effectively carried out by embedding to the host a computed sequence, which is obtained by solving an optimization problem whose parameters are known to the information hider but unknown to the attacker. An essential emphasis of the proposed method is randomization, which is crucial for security and robustness against arbitrary quality-preserving attacks. We formally show that malicious optimal estimation attacks that are specifically derived for our algorithm are ineffective in practice. Furthermore, we experimentally demonstrate that our watermarking method survives many generic benchmark attacks for a large number of images.     

A generic online acceleration scheme for optimization algorithms via relaxation and inertia

We propose generic acceleration schemes for a wide class of optimization and iterative schemes based on relaxation and inertia. In particular, we introduce methods that automatically tune the acceleration coefficients online and establish their convergence. This is made possible by considering classes of fixed-point iterations over averaged operators which encompass gradient methods, ADMM (Alternating Direction Method of Multipliers), primal dual algorithms and so on.     

PSO和ABC的混合优化算法

通过将粒子群优化(Particle Swarm Optimization,PSO)算法与人工蜂群(Artificial Bee Colony,ABC)算法相结合,提出一种ABC-PSO并行混合优化算法。在每次迭代中,将种群分为两个子种群,一个子种群使用PSO算法,另一个子种群使用ABC算法,两个算法寻优后进行比较,选出最优适应值。通过混合算法对4个标准函数进行测试,并与标准PSO算法进行比较,结果表明混合算法具有更好的优化性能。     

Two‐dimensional synthesis and optimization of a broadband shaped beam reflector antenna using IWO and PSO algorithms

A new technique for broadband shaped beam reflector antennas synthesis is presented. The surface of the shaped reflector is represented by a set of orthogonal Jacobi–Fourier expansion functions. To achieve a cosecant squared pattern in the elevation plane and a pencil beam in the azimuth plane, the expansion coefficients are optimized using invasive weed optimization and particle swarm optimization algorithms. High accuracy is achieved by combining optimization tools and analysis methods such as physical optics and integral equation. Besides, in the optimization procedures, a complex object function is used to achieve the desired performance over the entire 26–40 GHz operating bandwidth. The simulation results via FEKO and CST Microwave Studio software packages prove the validity and versatility of this technique for solving shaped reflector synthesis problems. There are several features that distinguish this technique from the previous methods. First of all is the wide bandwidth. The second is its ability to shape the radiation patterns in the elevation and azimuth planes simultaneously (two‐dimensional synthesis). Moreover, compared with other techniques, the proposed method allows achieving extra desired features such as high directivity, low sidelobe levels, and small ripples in the shaped beam region. © 2014 Wiley Periodicals, Inc. Int J RF and Microwave CAE 25:129–140, 2015.     

Branch-and-bound and PSO algorithms for no-wait job shop scheduling

This paper deals with the no-wait job shop scheduling problem resolution. The problem is to find a schedule to minimize the makespan (\(C_{max}\)), that is, the total completeness time of all jobs. The no-wait constraint occurs when two consecutive operations in a job must be processed without any waiting time either on or between machines. For this, we have proposed two different resolution methods, the first is an exact method based on the branch-and-bound algorithm, in which we have defined a new technique of branching. The second is a particular swarm optimization (PSO) algorithm, extended from the discrete version of PSO. In the proposed algorithm, we have defined the particle and the velocity structures, and an efficient approach is developed to move a particle to the new position. Moreover, we have adapted the timetabling procedure to find a good solution while respecting the no-wait constraint. Using the PSO method, we have reached good results compared to those in the literature.     

Analysis and design of an analog sorting network

An analog sorting neural network is presented. First, existing order representations are discussed and a generalized order representation is introduced. The sorting problem is then formulated as the assignment problem. Based on the assignment problem formulation, the neural network architecture is described. Design principles and an op-amp based circuit realization of the analog neural network are delineated. Three illustrative examples are also discussed to demonstrate the capability and performance of the analog neural network. The proposed analog neural network is shown to be capable of monotonic and bitonic sorting and suitable for hardware implementation.     

Forecasting output of integrated circuit industry by support vector regression models with marriage honey-bees optimization algorithms

Integrated circuit (IC) is a vital component of most electronic commodity. IC manufacturing in Taiwan is booming, with revenues from the ICs industry having grown significantly in the recent years. Given the nature of technology, capital intensity and high value-added, accurate forecasting of IC the industry output can improve the competitivity of IC cooperation. Support vector regression (SVR) is an emerging forecasting scheme that has been successfully adopted in many time-series forecasting areas. Additionally, the data preprocessing procedure and the determination of SVR parameters significantly impact the forecasting accuracy of SVR models. Thus, this work develops a support vector regression model with scaling preprocessing and marriage in honey-bee optimization (SVRSMBO) model to accurately forecast IC industry output. The scaling preprocessing procedure is utilized to lower the fluctuation of input data, and the marriage in honey-bees optimization (MBO) algorithm is adopted to determine the three parameters of the SVR model. Numerical data collected from the previous literature are used to demonstrate the performance of the proposed SVRSMBO model. Simulation results indicate that the SVRSMBO model outperforms other forecasting models. Hence, the SVRSMBO model is a promising means of forecasting IC industry output.     

Applied optimization and circuit design

A qualitative review of applied optimization forms is given, illustrated by reference to the linear circuit design problem. When used to solve very difficult problems, optimization theory becomes extremely vague with many conflicting objectives. These opposing standpoints are discussed and an approach is described which attempts to handle the complications. Working online, many of these nebulous aspects can be considered at run-time. To illustrate this approach a lumped-element simulation of a coaxial cable over a frequency band of $\text{1}\text{2}$ MHz-1 GHz is described.     

神经网络和改进粒子群算法在地震预测中的应用

提出了一种基于神经网络与改进粒子群算法的地震预测方法,该方法采用前向神经网络作为地震震级的预测模型,引入改进的粒子群算法对前向网络的连接权值进行修正。为了设计在全局搜索和局部搜索之间取得最佳平衡的惯性权重,基于粒子动态变异思想对粒子群优化算法进行改进,提出了一种动态变异粒子群优化算法,并将其应用于地震震级预测神经网络模型优化。在仿真实验中,将所提出的方法与另外两个采用不同算法的前向网络预测方法进行了比较。结果表明所提出的优化算法收敛速度最快,所得模型的预测误差最小,泛化能力最强,对地震的中期预测有很好的参考作用。     

便携式心电仪中模拟电路的设计

为了能将强噪音背景下的低频微弱心电信号无失真的提取出来,论文设计了集心电放大、右驱、滤波、和电平抬升为一体的模拟心电采集模块,并完成了电路的搭建和测试。试验证明本设计基本滤除了工频干扰,基线漂移和其它高频干扰,从而使心电波形的轮廓清晰可见。     

姿态参数的模拟解算电路设计与分析

根据姿态传感器输出与姿态参数之间的三角函数关系,设计了一种基于函数发生器的反三角函数电路,以减小模拟反三角函数解算误差,实现对姿态参数的直接解算。利用离散和连续2种方法对所设计电路进行测试,离散测试分析中采用多点拟合对实验数据进行处理,平均角度误差减小为0.11°,最大角度误差减小为2.87°;连续测试中对输入输出信号时域和频域特征进行了分析,测得输出波形的总谐波失真为8.14%。结果表明:解算方案可行,可用于姿态参数的模拟解算。