Solar Radiation Estimation Based on a New Combined Approach of Artificial Neural Networks (ANN) and Genetic Algorithms (GA) in South Algeria

When designing solar systems and assessing the effectiveness of their many uses,estimating sun irradiance is a crucial first step.This study examined three approaches(ANN,GA-ANN,and ANFIS)for estimating daily global solar radiation(GSR)in the south of Algeria:Adrar,Ouargla,and Bechar.The proposed hybrid GA-ANN model,based on genetic algorithm-based optimization,was developed to improve the ANN model.The GA-ANN and ANFIS models performed better than the standalone ANN-based model,with GA-ANN being better suited for forecasting in all sites,and it performed the best with the best values in the testing phase of Coefficient of Determination(R=0.9005),Mean Absolute Percentage Error(MAPE=8.40%),and Relative Root Mean Square Error(rRMSE=12.56%).Nevertheless,the ANFIS model outperformed the GA-ANN model in forecasting daily GSR,with the best values of indicators when testing the model being R=0.9374,MAPE=7.78%,and rRMSE=10.54%.Generally,we may conclude that the initial ANN stand-alone model performance when forecasting solar radiation has been improved,and the results obtained after injecting the genetic algorithm into the ANN to optimize its weights were satisfactory.The model can be used to forecast daily GSR in dry climates and other climates and may also be helpful in selecting solar energy system installations and sizes.

SELF-LEARNING FUZZY CONTROL RULES USING GENETIC ALGORITHMS

This papcr presents a new genetic algorithms(GAs)-based method for self-learniag fuzzy control rules. An improved GA is used to learn to optimally select the fuzzy membership functions of the linguistic labels in the condition portion of each rule, and to automatically generate fuzzy control actions under each condition. The dynamics of the controlled system is unknown to the GA. The only information for evaluating performance is a failure signal indicating that the controlled system is out of control. We compare its performance with that of other learning methods for the same problem. We also examine the ability of the algorithm to adapt to changing conditions. Simulation results show that such an approach for self-learning fuzzy control rules is both effective and robust.

Self-learning Fuzzy Controllers Based On a Real-time Reinforcement Genetic Algorithm

This paper presents a novel method for constructing fuzzy controllers based on a real time reinforcement genetic algorithm. This methodology introduces the real-time learning capability of neural networks into globally searching process of genetic algorithm, aiming to enhance the convergence rate and real-time learning ability of genetic algorithm, which is then used to construct fuzzy controllers for complex dynamic systems without any knowledge about system dynamics and prior control experience. The cart-pole system is employed as a test bed to demonstrate the effectiveness of the proposed control scheme, and the robustness of the acquired fuzzy controller with comparable result.

Application of the Adaptive Neuro-Fuzzy Inference System for Optimal Design of Reinforced Concrete Beams

Using a genetic algorithm owing to high nonlinearity of constraints, this paper first works on the optimal design of two-span continuous singly reinforced concrete beams. Given conditions are the span, dead and live loads, compressive strength of concrete and yield strength of steel;design variables are the width and effective depth of the continuous beam and steel ratios for positive and negative moments. The constraints are built based on the ACI Building Code by considering the strength requirements of shear and the maximum positive and negative moments, the development length of flexural reinforcement, and the serviceability requirement of deflection. The objective function is to minimize the total cost of steel and concrete. The optimal data found from the genetic algorithm are divided into three groups: the training set, the checking set and the testing set for the use of the adaptive neuro-fuzzy inference system (ANFIS). The input vector of ANFIS consists of the yield strength of steel, compressive strength of concrete, dead load, span, width and effective depth of the beam;its outputs are the minimum total cost and optimal steel ratios for positive and negative moments. To make ANFIS more efficient, the technique of Subtractive Clustering is applied to group the data to help streamline the fuzzy rules. Numerical results show that the performance of ANFIS is excellent, with correlation coefficients between the three targets and outputs of the testing data being greater than 0.99.

Blind Image Quality Assessment Based on Hybrid Fuzzy-Genetic Technique

A new method for no-reference image quality assessment based on hybrid fuzzy-genetic technique is proposed. Noise variance and edge sharpness level of the restored image are two basic metrics for assessing the performance of the restoration algorithm, then a fuzzy if-then inference system is developed to combine the two metrics to get a final quality score, and the parameters of the fuzzy membership function are trained with genetic algorithms. Experiments results show that the image quality score correlates well with mean opinion score and the proposed approach is robust and effective.

Quantum Algorithm of Imperfect KB Self-organization Pt I: Smart Control-Information-Thermodynamic Bounds

The quantum self-organization algorithm model of wise knowledge base design for intelligent fuzzy controllers with required robust level considered.Background of the model is a new model of quantum inference based on quantum genetic algorithm.Quantum genetic algorithm applied on line for the quantum correlation’s type searching between unknown solutions in quantum superposition of imperfect knowledge bases of intelligent controllers designed on soft computing.Disturbance conditions of analytical information-thermodynamic trade-off interrelations between main control quality measures(as new design laws)discussed in Part I.The smart control design with guaranteed achievement of these trade-off interrelations is main goal for quantum self-organization algorithm of imperfect KB.Sophisticated synergetic quantum information effect in Part I(autonomous robot in unpredicted control situations)and II(swarm robots with imperfect KB exchanging between“master-slaves”)introduced:a new robust smart controller on line designed from responses on unpredicted control situations of any imperfect KB applying quantum hidden information extracted from quantum correlation.Within the toolkit of classical intelligent control,the achievement of the similar synergetic information effect is impossible.Benchmarks of intelligent cognitive robotic control applications considered.

城市轨道交通网络跨线列车开行方案优化模型研究

通过开行跨线列车减少乘客换乘次数可有效提升城市轨道交通运输服务水平,本文结合线网客流特征提出跨线方案备选方法,构建跨线列车开行方案优化模型。首先,根据研制的出行路径推算系统分析线网全日各类客流量及比例,确定线网跨线列车的跨线次数,获得备选跨线长交路集;其次,以乘客换乘次数最小化为目标构建满足运营基本条件和跨线能力约束的跨线列车开行方案模型,并嵌套基于发车频率客流分配的改进遗传算法求解,获得本线交路和跨线交路的开行频率;最后,以某城市轨道交通线网为例,研究跨线列车开行方案的优化效果。结果表明:在换乘站开行跨线长交路,全网换乘乘客总换乘次数减少了2.02%~5.97%,降低乘客换乘时间和线网换乘系数,同时直达客流增加了1.58%~4.58%;跨线长交路在衔接线路上承担小交路以补充断面运力,减少本线交路列车的开行频率,可减少线网的上线列车总数和列车正线走行公里数;此外,当换乘站的换乘客流量较高时,跨线长交路因衔接线路上本线交路列车开行频率较高而无法实施,且随着跨线交路开行频率接近跨线能力上限,运营服务水平的提升效果呈逐渐降低趋势。

基于遗传模糊推理系统的截击点智能解算方法

由于传统截击引导需要人的参与才能完成截击任务,截击方案生成速度要受到人反应速度的限制,成为提高作战速度的一个瓶颈。针对空中战场态势变化快、依靠人工制定拦截方案难以适应未来空战的问题,构建基于模糊推理系统的空战截击点智能解算模型,并采用分段变异和变步长寻优的遗传算法对模型进行训练,实现了对截击方案的快速估算,得到在解空间内性能可以达到甚至超越人类的截击点智能解算模型。

Element yield rate prediction in ladle furnace based on improved GA-ANFIS

The traditional prediction methods of element yield rate can be divided into experience method and data-driven method.But in practice,the experience formulae are found to work only under some specific conditions,and the sample data that are used to establish data-driven models are always insufficient.Aiming at this problem,a combined method of genetic algorithm(GA) and adaptive neuro-fuzzy inference system(ANFIS) is proposed and applied to element yield rate prediction in ladle furnace(LF).In order to get rid of the over reliance upon data in data-driven method and act as a supplement of inadequate samples,smelting experience is integrated into prediction model as fuzzy empirical rules by using the improved ANFIS method.For facilitating the combination of fuzzy rules,feature construction method based on GA is used to reduce input dimension,and the selection operation in GA is improved to speed up the convergence rate and to avoid trapping into local optima.The experimental and practical testing results show that the proposed method is more accurate than other prediction methods.

Modeling the spread of spatio-temporal phenomena through the incorporation of ANFIS and genetically controlled cellular automata: a case study on forest fire

Virtual representation and simulation of spatio-temporal phenomena is a promising goal for the production of an advanced digital earth.Spread modeling,which is one of the most helpful analyses in the geographic information system(GIS),plays a prominent role in meeting this objective.This study proposes a new model that considers both aspects of static and dynamic behaviors of spreadable spatio-temporal in cellular automata(CA)modeling.Therefore,artificial intelligence tools such as adaptive neuro-fuzzy inference system(ANFIS)and genetic algorithm(GA)were used in accordance with the objectives of knowledge discovery and optimization.Significant conditions in updating states are considered so traditional CA transition rules can be accompanied with the impact of fuzzy discovered knowledge and the solution of spread optimization.We focused on the estimation of forest fire growth as an important case study for decision makers.A two-dimensional cellular representation of the combustion of heterogeneous fuel types and density on non-flat terrain were successfully linked with dynamic wind and slope impact.The validation of the simulation on experimental data indicated a relatively realistic head-fire shape.Further investigations showed that the results obtained using the dynamic controlling with GA in the absence of static modeling with ANFIS were unacceptable.

Temperature modeling and control of Direct Methanol Fuel Cell based on adaptive neural fuzzy technology

Aiming at on-line controlling of Direct Methanol Fuel Cell （DMFC） stack, an adaptive neural fuzzy inference technology is adopted in the modeling and control of DMFC temperature system. In the modeling process, an Adaptive Neural Fuzzy Inference System （ANFIS） identification model of DMFC stack temperature is developed based on the input-output sampled data, which can avoid the internal complexity of DMFC stack. In the controlling process, with the network model trained well as the reference model of the DMFC control system, a novel fuzzy genetic algorithm is used to regulate the parameters and fuzzy rules of a neural fuzzy controller. In the simulation, compared with the nonlinear Proportional Integral Derivative （PID） and traditional fuzzy algorithm, the improved neural fuzzy controller designed in this paper gets better performance, as demonstrated by the simulation results.

基于遗传算法的球度误差评定(英文)

首先对球度公差评定问题进行了综述。然后根据圆度公差的数学定义 ,引申提出球度公差最小区域条件下的评定模型 ,并给出遗传算法的适应度函数。随后给出算法实现中的关键问题。最后用实例对算法进行了检验 ,计算结果表明基于遗传算法的球度误差优化算法不仅符合最小区域条件 ,而且易于理解和实现 ,能够获得全局最优解 ,保证了高精度。

模糊神经网络和遗传算法结合的船舶火灾探测

本文提出了一种模糊推理系统、神经网络和遗传算法相结合的船舶火灾探测算法。该方法用神经网络来构造模糊推理系统 ,通过遗传算法对神经网络进行训练来实现船舶火灾的分级报警。

统计遗传算法

本文讨论了遗传算法中框架定理的不足之处，并对之进行了改进，然后分析了遗传算法与Ａ算法的相似性，以及遗传算法的概率性质．由此联想到它与ＳＡ算法的相似性，在此基础上，作者将原先发展的一套ＳＡ算法的理论移植到遗传算法中来，建立一个新的算法，称之为统计遗传算法（简记为ＳＧＡ算法）．为适合于优化计算，作者引入最大值统计量及其对应的ＳＡ算法（简称为ＳＭＡ算法），并将ＳＭＡ算法与ＧＡ算法相结合（记为ＳＧＡ（ＭＡＸ）算法）．新的算法不仅提高了算法的精度和降低了计算的复杂性，而且能克服ＧＡ算法中出现“早熟”的现象以及提供进行并行计算的可能性．更主要的是新的方法为ＧＡ算法的精度。

基于自适应神经-模糊推理系统和遗传算法的桥梁耐久性评估

将模糊推理、神经网络、遗传算法三种技术有机融合在一起,建立了基于自适应神经-模糊推理系统(ANFIS)和遗传算法(GAS)的桥梁耐久性评估专家系统。该系统将专家的模糊推理过程蕴含于神经网络结构中,使神经网络的节点和权值具有明确的物理意义,避免了传统神经网络工作过程的“黑盒”性。同时该系统又具有神经网络的自适应性和学习能力,克服了传统模糊推理系统学习能力差的缺点。而且采用遗传和反向传播相结合的GA-BP混合算法训练网络,充分发挥了遗传算法的全局搜索性和BP的局部微调快速性的优点。并以辽宁省13座桥300根梁的实测数据对其进行训练和测试,系统输出与期望输出吻合较好,证明该专家系统性能稳定、有效,具有实用价值。

遗传算法在故障诊断专家系统中的应用

介绍了遗传算法在故障诊断专家系统的推理和在自学习中的应用，克服了专家系统存在的推理速度慢和先验知识很少情况下知识获取困难的障碍；并将该方法应用于运输链直流调速系统，取得了良好的效果。

基于模糊自适应模拟退火遗传算法的配电网故障定位

对于配电网故障定位系统的不足与遗传算法存在易早熟、收敛速度慢等问题,结合模糊推理和自适应模拟退火遗传算法,提出一种模糊自适应模拟退火遗传算法(FASAGA)。该算法对评价函数做了容错性改进,在遗传选择时采用自适应机制与最佳个体保留策略,并结合模糊推理与自适应机制求取模糊自适应交叉算子、模糊自适应变异算子,引入模拟退火算法提高收敛速度与局部搜索能力。仿真结果说明该算法应用在配电网故障定位中的准确性、快速性与高容错性。

智能PID控制在电石炉电极调节系统中的应用

针对三相电极电流耦合的电石炉系统,设计出一种智能PID控制器。该控制器首先采用实数编码的遗传算法优化PID控制器的参数,得到一组参数的最优值。然后以此最优值作为PID参数的初始值,结合积分分离的原则设计出一种模糊解耦推理规则对PID参数进行实时整定,以确保系统的响应具有最优的动态和稳态性能。对比仿真试验的结果表明,与常规的PID控制器相比,这种智能PID控制器用于电石炉控制系统可达到良好的动、静态性能和鲁棒性。

基于遗传算法的模糊规则的生成

模糊控制是人工智能的一重要研究领域 ,已经在很多方面得到了应用。模糊规则是一个智能系统的核心部分 ,所以模糊规则自动生成的研究一直以来吸引了很多的学者。遗传算法 (GeneticAlgorithm ,GA)是模拟达尔文的遗传选择和自然淘汰的生物进化进程的计算模型 ,它是一种高度并行的随机化搜索的自适应的组合优化算法。该文提出了一种利用遗传算法自动生成模糊规则的方法 ,因为遗传算法的全局优化能力 ,所以可以得到相对较为合适的模糊规则 ,通过仿真结果 。