基于模糊推理的碟式太阳能跟踪系统改进研究

在碟式太阳能系统的长期实验数据中发现,太阳辐照度较低时系统集热效率非常低。为使碟式太阳能系统始终运行在高集热效率状态,提出模糊推理方法改进跟踪系统的启停判定条件,并根据集热器出口气流温度的影响因素建立模糊推理系统。仿真结果表明模糊系统在已知太阳辐照度、辐照变化度和空气流量的前提下,可智能控制系统在集热效率高时运行,集热效率低时待机,证明了该方法的可行性和有效性。     

GA-based learning for rule identification in fuzzy neural networks

Employing an effective learning process is a critical topic in designing a fuzzy neural network, especially when expert knowledge is not available. This paper presents a genetic algorithm (GA) based learning approach for a specific type of fuzzy neural network. The proposed learning approach consists of three stages. In the first stage the membership functions of both input and output variables are initialized by determining their centers and widths using a self-organizing algorithm. The second stage employs the proposed GA based learning algorithm to identify the fuzzy rules while the final stage tunes the derived structure and parameters using a back-propagation learning algorithm. The capabilities of the proposed GA-based learning approach are evaluated using a well-examined benchmark example and its effectiveness is analyzed by means of a comparative study with other approaches. The usefulness of the proposed GA-based learning approach is also illustrated in a practical case study where it is used to predict the performance of road traffic control actions. Results from the benchmarking exercise and case study effectively demonstrate the ability of the proposed three stages learning approach to identify relevant fuzzy rules from a training data set with a higher prediction accuracy than alternative approaches.     

Learning fuzzy controllers in mobile robotics with embedded preprocessing

The automatic design of controllers for mobile robots usually requires two stages. In the first stage, sensorial data are preprocessed or transformed into high level and meaningful values of variables which are usually defined from expert knowledge. In the second stage, a machine learning technique is applied to obtain a controller that maps these high level variables to the control commands that are actually sent to the robot. This paper describes an algorithm that is able to embed the preprocessing stage into the learning stage in order to get controllers directly starting from sensorial raw data with no expert knowledge involved. Due to the high dimensionality of the sensorial data, this approach uses Quantified Fuzzy Rules (QFRs), that are able to transform low-level input variables into high-level input variables, reducing the dimensionality through summarization. The proposed learning algorithm, called Iterative Quantified Fuzzy Rule Learning (IQFRL), is based on genetic programming. IQFRL is able to learn rules with different structures, and can manage linguistic variables with multiple granularities. The algorithm has been tested with the implementation of the wall-following behavior both in several realistic simulated environments with different complexity and on a Pioneer 3-AT robot in two real environments. Results have been compared with several well-known learning algorithms combined with different data preprocessing techniques, showing that IQFRL exhibits a better and statistically significant performance. Moreover, three real world applications for which IQFRL plays a central role are also presented: path and object tracking with static and moving obstacles avoidance.     

Prediction of rock mass P wave velocity using blasthole drilling information

In this paper, the development of the models for the prediction of rock mass P wave velocity is presented. For model development, the database of 53 cases including widely used and recorded drilling parameters and P wave velocity was constructed from the field studies conducted in 13 open pit lignite mines. Both conventional linear, non-linear multiple regression and Adaptive Neuro Fuzzy Inference System (ANFIS) were used for model development. Prediction performance indicators showed that ANFIS model presented the best performance and it can successfully be used for the preliminary prediction of P wave velocities of rock masses.     

A review on modeling of solar photovoltaic systems using artificial neural networks,fuzzy logic,genetic algorithm and hybrid models

The uncertainty associated with modeling and performance prediction of solar photovoltaic systems could be easily and efficiently solved by artificial intelligence techniques. During the past decade of 2009 to 2019, artificial neural network (ANN), fuzzy logic (FL), genetic algorithm (GA) and their hybrid models are found potential artificial intelligence tools for performance prediction and modeling of solar photovoltaic systems. In addition, during this decade there is no extensive review on applicability of ANN, FL, GA and their hybrid models for performance prediction and modeling of solar photovoltaic systems. Therefore, this article focuses on extensive review on design, modeling, maximum power point tracking, fault detection and output power/efficiency prediction of solar photovoltaic systems using artificial intelligence techniques of the ANN, FL, GA and their hybrid models. In addition, the selected articles on the solar radiation prediction using ANN, FL, GA and their hybrid models are also summarized. Total of 122 articles are reviewed and summarized in the present review for the period of 2009 to 2019 with 90 articles in the field of {ANN, FL, GA and their hybrid models} + solar photovoltaic systems and 32 articles in the field of {ANN, FL, GA and their hybrid models} + solar radiation. The review shows the suitability and reliability of ANN, FL, GA and hybrid models for accurate prediction of the solar radiation and the performance characteristics of solar photovoltaic systems. In addition, this review presents the guidance for the researchers and engineers in the field of solar photovoltaic systems to select the suitable prediction tool for enhancement of the performance characteristics of the solar photovoltaic systems and the utilization of the available solar radiation.     

Study on the influence of maintenance operations on energy consumption and emissions in healthcare centres by fuzzy cognitive maps

The present work is aimed at performing a qualitative analysis of the effect of maintenance intensity on energy consumption, energy costs and emissions in healthcare centres. Most relevant variables involved in the maximization of the building efficiency were determined by means of fuzzy cognitive maps. Twelve variables were observed to show a direct connection to the energy and environmental efficiency as well as to its maintenance condition. The joint effect of these factors was seen to improve the overall performance of the building in terms of efficiency. However, even though maintenance appeared as one of the most highly-rated variables, it did not seem to affect the overall performance by itself, but it was rather due to the synergistic action of the remaining variables. The behaviour of those variables was evidenced to show a significant change for buildings below 1250?m² floor area as compared to those exceeding such threshold value.     

Prioritizing the elective surgery patient admission in a Chinese public tertiary hospital using the hesitant fuzzy linguistic ORESTE method

In public health systems around the world, there are not enough medical resources to provide elective (e.g., scheduled or non-emergency) services for all patients immediately. One feasible solution is to prioritize patients by taking into account a variety of factors, such as disease severity, waiting time, and disease types. This is a typical Multiple Criteria Decision Making (MCDM) problem. To solve this problem, in this paper, we first conduct an investigation on the admission process, and obtain 16 indicators affecting patients’ admission, which form a criteria system. Since there is much vague and uncertain information which can be depicted by the hesitant fuzzy linguistic term set effectively for these indicators, we then apply a powerful MCDM method, named the hesitant fuzzy linguistic ORESTE, to prioritize the elective surgery patient admission in a Chinese public tertiary hospital, the West China Hospital. Robust results are obtained by performing a sensitivity analysis with six scenarios. We also compare the results with those derived by other HFL-MCDM methods. It is illustrated that the hesitant fuzzy linguistic ORESTE can help hospitals flexibly manage the patient admissions.     

基于模糊多元联系度模型的尾矿库综合安全评价

为准确评价尾矿库的安全等级，以黄金洞尾矿库为例，分析影响尾矿库安全的各因素之间的关系，综合运用模糊多元联系度理论与层次分析法（AHP），建立了包含5个影响因素和26个影响因子的尾矿库综合安全评价模型。由AHP得出各项主、客观指标的权重系数，引入模糊多元联系度理论，综合考虑确定性与不确定性因素，处理综合集成问题，构造隶属函数量化指标的实测值。结果表明：黄金洞尾矿库安全等级为Ⅱ级，尾矿库处于较稳定状态。经比较不确定性因素大小，得出尾矿排放（B₃）>尾矿坝（B₂）>安全管理（B₁）>尾矿输送与回水（B₅）>排洪系统（B₄），说明尾矿排放指标的不确定性因素最大，需对尾矿排放加强管理。该模型得到的评价结果与实际结果相吻合，为尾矿库安全性评价提供了一种可行的方法。     

Integral barrier Lyapunov function-based adaptive fuzzy output feedback control for nonlinear delayed systems with time-varying full-state constraints

In this article, an adaptive fuzzy output feedback control method is presented for nonlinear time-delay systems with time-varying full state constraints and input saturation. To overcome the problem of time-varying constraints, the integral barrier Lyapunov functions (IBLFs) integrating with dynamic surface control (DSC) are applied for the first time to keep the state from violating constraints. The effects of unknown time delays can be removed by using designed Lyapunov-Krasovskii functions (LKFs). An auxiliary design system is introduced to solve the problem of input saturation. The unknown nonlinear functions are approximated by the fuzzy logic systems (FLS), and the unmeasured states are estimated by a designed fuzzy observer. The novel controller can guarantee that all signals remain semiglobally uniformly ultimately bounded and satisfactory tracking performance is achieved. Finally, two simulation examples illustrate the effectiveness of the presented control methods.