遗传优化支持向量机在电力负荷预测中的应用

研究电力负荷准确预测问题,电力负荷与影响因子之间呈现复杂非线性关系,传统预测方法无法刻画其变化规律,预测精度低。为提高电力负荷预测精度,提出一种采用遗传优化支持向量机的电力负荷预测模型。采用最小二乘支持向量机的非线性逼近能力去描述电力负荷与影响因子间的复杂非线性关系,并采用自适应遗传算法优化最小二乘支持向量机的参数。采用某省1990～2008年电力负荷数据仿真测试,结果表明,遗传优化支持向量机提高了电力负荷的预测精度,预测平均误差低于其它对比模型,电力负荷预测提供了一种新的研究思路和途径。     

一种基于自适应遗传算法的统一潮流控制器

提出一种自适应遗传算法的智能控制策略,并将其应用于统一潮流控制器的多变量控制中.对3个通用的标准测试函数进行了优化,将其测试结果与简单遗传算法进行了比较.仿真结果表明:自适应机制提高了算法的搜索性能,与传统PI控制相比具有更强的动态性和鲁棒性。     

交流伺服系统最优模糊控制器设计

PID控制器性能好坏的关键在于比例、积分和微分参数的选择.传统PID调节器中参数的选择主要依靠反复调试和经验知识,但由于参数之间的互相影响以及PID调节器对被控对象的鲁棒性较差,因此将其应用于交流伺服系统中难以取得满意的效果.根据交流伺服系统高精度、快响应的要求,设计了一种应用于交流伺服系统的基于适应性遗传算法优化的交流伺服系统模糊PID控制器.试验结果表明:该控制方法响应快、鲁棒性强,系统具有较好的动、静态性能和抗干扰能力.     

基于HAGA的D2D-NOMA资源分配优化算法

针对无线系统带宽资源有限、基站负载压力大、传输时延长等问题,提出一种基于非正交多址接入技术的D2D系统吞吐量最大化资源分配算法。在不同用户的服务质量约束条件下,建立D2D系统吞吐量最大化资源分配模型。该模型的优化目标是一个混合整数非线性规划问题,将其解耦为信道匹配与功率分配2个子问题并分别进行处理,利用自适应惩罚函数法处理约束条件并提出一种基于爬山策略的自适应遗传算法以对问题进行求解。仿真结果表明,与GA、AGA算法相比,该算法能够有效提高D2D系统的吞吐量,且收敛性能更好。     

Reassigning Sense Codon AGA to Encode Noncanonical Amino Acids in Escherichia coli

A new method has been developed to reassign the rare codon AGA in Escherichia coli by engineering an orthogonal tRNA/aminoacyl–tRNA synthetase pair derived from Methanocaldococcus jannaschii. The tRNA mutant was introduced with a UCU anticodon, and the synthetase was evolved to correctly recognize the modified tRNA anticodon loop and to selectively charge a target noncanonical amino acid (NAA) onto the tRNA. In order to maximize the efficiency of AGA codon reassignment, while avoiding the lethal effects caused by global codon reassignment in cellular proteins, an inducible promoter (araBAD) was utilized to provide temporal controls for overexpression of the aminoacyl–tRNA synthetase and switch on codon reassignment. Using this system, we were able to efficiently incorporate p‐acetylphenylalanine, O‐methyl‐tyrosine, and p‐iodophenylalanine into proteins in response to AGA codons. Also, we found that E. coli strain GM10 was optimal in achieving the highest AGA reassignment rates. The successful reassignment of AGA codons reported here provides a new avenue to further expand the genetic code.     

AGA8—92DC计算方法天然气压缩因子计算

按照GB／T 17747．2—1999《天然气压缩因子的计算第2部分：用摩尔组成进行计算》,采用AGA8—92DC计算方法,用VB编程计算了天然气压缩因子。用二分法求解状态方程,精度满足工程需要。     

Estimating the kinetic parameters of activated sludge storage using weighted non-linear least-squares and accelerating genetic algorithm

In this study, weighted non-linear least-squares analysis and accelerating genetic algorithm are integrated to estimate the kinetic parameters of substrate consumption and storage product formation of activated sludge. A storage product formation equation is developed and used to construct the objective function for the determination of its production kinetics. The weighted least-squares analysis is employed to calculate the differences in the storage product concentration between the model predictions and the experimental data as the sum of squared weighted errors. The kinetic parameters for the substrate consumption and the storage product formation are estimated to be the maximum heterotrophic growth rate of 0.12 1/h, the yield coefficient of 0.44 mg COD_X/mg COD_S (COD, chemical oxygen demand) and the substrate half saturation constant of 16.9 mg/L, respectively, by minimizing the objective function using a real-coding-based accelerating genetic algorithm. Also, the fraction of substrate electrons diverted to the storage product formation is estimated to be 0.43 mg COD_STO/mg COD_S. The validity of our approach is confirmed by the results of independent tests and the kinetic parameter values reported in literature, suggesting that this approach could be useful to evaluate the product formation kinetics of mixed cultures like activated sludge. More importantly, as this integrated approach could estimate the kinetic parameters rapidly and accurately, it could be applied to other biological processes.     

Thermodynamic analysis of natural gas reciprocating compressors based on real and ideal gas models

The accurate modelling and investigating effects of various parameters of the reciprocating compressors are important subjects. In this work, based on first law of thermodynamics, conversation of mass and real and ideal gas assumptions, a theoretical analysis has been constructed to simulate natural gas reciprocating compressors. For computing the thermodynamic properties of natural gas based on real gas model, the AGA8 equation of state has been used. Numerical results validated with previous measured values and showed a good agreement. The effects of important parameters such as: angular speed, clearance and pressure ratio have been studied on the performance of the compressors. The results reveal the in-control volume temperature for ideal gas is more than real gas model but the mass flow rate and work for real gas is higher than ideal gas model. On the other hand, the indicated work that required for compression is greater for ideal gas model.     

基于果蝇算法与自适应性遗传算法组合优化神经网络的光伏电站短期出力预测

光伏电站的输出功率会随着很多因素发生波动,若能够提高光伏系统出力预测的准确性,则能有效地降低光伏电站并网后对电网造成的冲击,提高电力系统的稳定性。建立了果蝇算法与自适应遗传算法组合优化的BP神经网络的预测模型。从预测结果可以发现,采用组合优化算法的BP神经网络模型能够有效避免地BP神经网络易陷入局部极小值点的缺陷,相比于仅优化权值和阈值的BP神经网络模型提高了预测精度,具有一定的应用价值。