船舶柴油机智能控制技术研究进展

船舶柴油机智能控制技术是将人工智能算法引入到船舶柴油机控制领域,提高复杂时变非线性系统的控制性能。重点针对基于智能控制算法设计的柴油机子系统控制器研究和基于非线性模型预测架构的柴油机在线性能优化研究,对船舶柴油机智能控制技术的研究现状和进展进行了综述,并分析了此研究领域所涉及的主要理论和方法。根据船舶柴油机智能控制不同应用需求,重点介绍了智能控制方法在船舶推进柴油机新型燃烧、复杂气系统、转速跟踪等控制问题中的应用,分析了智能控制方法的优缺点,讨论了在船舶柴油机控制领域中智能控制技术的未来研究方向和面临的挑战。     

神经网络在液体火箭发动机故障检测中的应用(Ⅰ)非线性辨识技术

应用ＢＰ神经网络,提出了一种液体火箭发动机故障实时检测系统。它采用非线性辨识技术,在建立发动机数学模型和输出包含故障信息的监视指标信号之后,用阈值线与监视指标比较,从而预报发动机故障。液体火箭发动机启动与稳态过程的试验数据检验表明：基于非线性辨识技术的故障检测系统性能优越。     

Correlation between sunspots and interplanetary shocks measured by ACE during 1998–2014 and some estimations for the 22nd solar cycle and the years between 2015 and 2018 with artificial neural network using the Cavus 2013 model

The Advanced Composition Explorer (ACE) spacecraft has measured 235 solar-based interplanetary (IP) shock waves between the years of 1998–2014. These were composed of 203 fast forward (FF), 6 slow forward (SF), 21 fast reverse (FR) and 5 slow reverse (SR) type shocks. These data can be obtained from the Interplanetary Shock Database of Harvard-Smithsonian Centre for Astrophysics. The Solar Section of American Association of Variable Star Observers (AAVSO) is an organization that counts the number of the sunspots. The effects of interplanetary shock waves on some physical parameters can be computed using a hydrodynamical model. There should be some correlations between these effects and the sunspot variations. The major objective of this paper is twofold. The first one is to search these correlations with sunspots given in the database of AAVSO. As expected, high correlations between physical parameters and sunspots have been obtained and these are presented in tables below. The second objective is to make an estimation of these parameters for the 22nd solar cycle and the years between 2015 and 2018 using an artificial neural network. Predictions have been made for these years where no shock data is present using artificial intelligence. The correlations were observed to increase further when these prediction results were included.     

Models for estimating daily photosynthetically active radiation in oceanic and mediterranean climates and their improvement by site adaptation techniques

In this work Photosynthetically Active Radiation (PAR) in oceanic and mediterranean climates is modeled. Twenty-two different models have been developed and tested: eleven Multilinear Regression (MR) models and eleven Artificial Neuron Network (ANN) models, using combinations of variables such as Global Horizontal Irradiance (GHI), Global Extraterrestrial Irradiance (G₀), Temperature (T) and Relative Humidity (RH). Data provided by Satellite Application Facility on Climate Monitoring (CM SAF) are used to develop and train the models, while the models have been validated using field data from four stations located in Spain, covering the different study climates.According to the results, zones with different climate conditions need different models, both for the case of MR and ANN. The results show the need of including the GHI in all models in order to obtain accurate estimates; in fact, the presence of more variables only improves slightly the results in mediterranean climate, while in oceanic climate no improvement is observed.On the other hand, comparing MR and ANN models, ANN models did not show better results than those of MR models in no one of the cases studied. Regarding the climate, both types of models are clearly better for the mediterranean case than for the oceanic one. In order to improve the performance of the model for oceanic climate a correction based on the site adaptation technique was carried out. The good results obtained by this technique fully justify its use.The best proposed models provide better performance than other models which are restricted to certain locations. Besides, the clustering technique based on the PAR variable, used in this work, allows obtaining useful models for a whole region. Finally, another advantage of this methodology is that there is no need of ground measurements for its development, except for the site adaptation technique.     

Regional modeling of the ionosphere using adaptive neuro-fuzzy inference system in Iran

In recent years, new techniques and algorithms such as Artificial Neural Networks (ANNs), Fuzzy Inference Systems (FIS) and Genetic Algorithm (GA) have been used as alternative statistical tools in modeling and forecasting issues. These methods have been extensively used in the field of geosciences and atmospheric physics. The main purpose of this paper is to combine FIS and ANNs for local modeling of the ionosphere Total Electron Content (TEC) in Iran. An Adaptive Neuro-Fuzzy Inference System (ANFIS) is developed for TEC modeling. Also, Multi-Layer Perceptron ANN (MLP-ANN) and ANN based on Radial Base Functions (RBF) have been designed for analyzing ANFIS results. Observations of 29 Global Positioning System (GPS) stations from the Iranian Permanent GPS Network (IPGN) have been used in 3 different seasons in 2015 and 2016. These stations are located at geomagnetic low latitudes region. Out of these 29 stations, 24 stations for training and 5 stations for testing and validating were selected. The relative and absolute errors have been used to evaluate the accuracy of the proposed model. Also, the results of this paper are compared with the International Reference Ionosphere model (IRI2016). The maximum values of the average relative error for RBF, MLP-ANN, ANFIS and IRI2016 methods are 13.88%, 11.79%, 10.06%, and 18.34%, respectively. Also, the maximum values of the average absolute error for these methods are 2.38, 2.21, 1.5 and 3.36 TECU, respectively. Comparison of diurnal predicted TEC from the ANFIS, RBF, MLP-ANN and IRI2016 models with GPS-TEC revealed that the ANFIS provides more accurate predictions than the other methods in the test area.     

One-week habitation of two humans in an airtight facility with two goats and 23 crops – Analysis of carbon,oxygen, and water circulation

Human habitation and animal holding experiments in a closed environment, the Closed Ecology Experiment Facilities (CEEF), were carried out. The CEEF were established for collecting experimental data to estimate carbon transfer in the ecosystem around Rokkasho nuclear fuel reprocessing plant. Circulation of O₂ and CO₂, and supply of food from crops cultivated in the CEEF were conducted for the first time in the habitation experiments. Two humans known as eco-nauts inhabited the CEEF, living and working in the Plant Module (PM) and the Animal and Habitation Module (AHM), for a week three times in 2005. On a fresh weight basis, 82% of their food was supplied from 23 crops including rice and soybean, cultivated and harvested in the PM, in the 2nd and 3rd experiments. For the goats, the animals held in the experiments, all of their feed, consisting of rice straw, soybean plant leaves, and peanut shells and peanut plant leaves, was produced in the PM in the 2nd and 3rd experiments. The O₂ produced in the PM by photosynthesis of the crops was separated by the O₂ separator using molecular sheaves, then accumulated, transferred, and supplied to the AHM atmosphere. The CO₂ produced in the AHM by respiration of the humans and animals was separated by the CO₂ separator using solid amine, then accumulated, transferred, and supplied to the PM atmosphere. The amount of O₂ consumed in the AHM was 46–51% of that produced in the PM, and the amount of CO₂ produced in the AHM was 43–56% of that consumed in the PM. The surplus of O₂ and the shortage of CO₂ was a result of the fact that waste of the goats and the crops and part of the human waste were not processed in these habitation experiments. The estimated amount of carbon ingested by the eco-nauts was 64–92% of that in the harvested edible part of the crops. The estimated amount of carbon ingested by the goats was 36–53% of that in the harvested inedible part of the crops. One week was not enough time for determination of gas exchange especially for humans and animals, because fluctuation of their gas exchange was quite high. The amount of transpired water collected as condensate was 818–938 L d⁻¹, and it was recycled as replenishing water compensating transpiration loss of nutrient solution. The amount of waste nutrient solution discharged from the PM was 1421–1644 L d⁻¹. The waste nutrient solutions from rice and other crops were processed through micro filters (MFs) separately. The MF filtrated solutions were processed with reverse osmosis (RO) membrane filter separately and divided into filtrated water and concentrated waste nutrient solution. The concentrated waste nutrient solution from the crops other than rice was processed through an ultra-micro filter (UF) and reused, although that from rice was discharged in 2005. Concentrations of nutritional ions in the UF filtrated solution were determined, the depleted ions were added back, the UF filtrated solution was diluted with the RO membrane filtrated water, and the nutrient solution for the crops other than rice was regenerated. The nutrient solution for rice was newly made each time, using concentrated solution from an external source and the RO membrane filtrated water. Average amounts of water used in the AHM (L d⁻¹) were determined as follows: drinking by humans (filtrated water), 1.5; cooking, etc. (filtrated water other than for drinking), 14.3; drinking by goats, 3.8; showering (hot water), 13.2; showering (cold water), 0.1; washing of hand and face and brushing teeth, 4.1; washing of dishes, dish clothes and towels, 36.4; and washing of animal holding tools, 0.3. The waste water was processed by a RO purification system and recycled for toilet flushing and animal pens washing. A circulation experiment for water was started in 2006 and a circulation experiment for waste materials is planned for 2007. In 2006, a single duration of the air circulation experiments was 2 weeks, although the human habitants were changed after 1 week.     

Comparison of ANN and MLR models for estimating solar radiation in Turkey using NOAA/AVHRR data

In this paper, the estimation capacities of MLR and ANN are investigated to estimate monthly-average daily SR over Turkey. The satellite data are used for 73 different locations over Turkey. Land surface temperature, altitude, latitude, longitude and month are offered as the input variables for modeling ANN and MLR to get SR. Estimations of SR are evaluated with the meteorological values by using the statistical bases. The obtained results indicated that the ANN model could achieve a satisfactory performance when compared to the MLR model. Moreover, it is understood that more accurate results in estimation of SR are obtained in the use of satellite data, rather than the use of meteorological station data. Finally, the built ANN model is used to estimate the yearly average of daily SR over Turkey. As a result, satellite-based SR map for Turkey is generated.     

高新技术在系统仿真中的应用前景

简述人工智能，多媒体和灵境技术的由来和发展，重点介绍了仿真领域应用这三项技术的最新动态。探讨了应用过程中的关键技术或存在的问题。     

人工智能产生式系统的分析(下)

本文分为四节:一、产生式系统的求解方式;二、问题描述的不同表示法;三、规则集的灵活性和规格化;四、控制策略。全文分上、下篇发表,上篇为第一、二、三节,发表于本刊1986年第2期,本期发表第四节,即下篇。     

一种基于小波和人工神经网络的故障检测与诊断方法

提出了一种基于小波和人工神经网络的故障检测与诊断的方法，该方法利用小波包分解的精确细分的特点，分别对正常系统和故障系统的采样信号进行精确特征提取，并构造一系列基于信号能量且具有表征系统状态能力的特征向量，然后利用人工神经网络分类器对系统在各种状态下的特征向量进行分类决策，从而实现对系统的故障检测与诊断。