A Non-Singleton Type-3 Fuzzy Modeling: Optimized by Square-Root Cubature Kalman Filter

In many problems,to analyze the process/metabolism behavior,a mod-el of the system is identified.The main gap is the weakness of current methods vs.noisy environments.The primary objective of this study is to present a more robust method against uncertainties.This paper proposes a new deep learning scheme for modeling and identification applications.The suggested approach is based on non-singleton type-3 fuzzy logic systems(NT3-FLSs)that can support measurement errors and high-level uncertainties.Besides the rule optimization,the antecedent parameters and the level of secondary memberships are also adjusted by the suggested square root cubature Kalmanfilter(SCKF).In the learn-ing algorithm,the presented NT3-FLSs are deeply learned,and their nonlinear structure is preserved.The designed scheme is applied for modeling carbon cap-ture and sequestration problem using real-world data sets.Through various ana-lyses and comparisons,the better efficiency of the proposed fuzzy modeling scheme is verified.The main advantages of the suggested approach include better resistance against uncertainties,deep learning,and good convergence.

APPLICATION OF FUZZY OPTIMIZATION MODEL IN ECOLOGICAL SECURITY PRE-WARNING

Ecological security is a vital problem that people all over the world today have to face and solve, and the situation of ecological security is getting more and more severe and has begun to impede heavily the sustainable development of social economy. Ecological environment pre-warning has become a hotspot for the modern environment science. This paper introduces the theories of ecological security pre-warning and tries to constitute a pre-warning model of ecological security. In terms of pressure-state-response model, the pre-warning guide line of ecological security is constructed while the pre-warning degree judging model of ecological security is established based on fuzzy optimization. As a case, the model is used to assess the present condition pre-warning of the ecological security of Anhui Province. The result is in correspondence with the real condition: the ecological security situations of 8 cities are dangerous and 9 cities are secure. The result shows that this model is scientific and effective for regional ecological security pre-warning.

Intelligent modeling and optimization on time-sharing power dispatching system for electrolytic zinc process

Based on real time price counting of electric power, an optimization model of time sharing power for electrolytic zinc process(EZP) was established by means of an incremental fuzzy neural network(FNN), which is adopted to approximate the relationship of current efficiency, current density and acidity. Penalty function introduced and optimal objective function reconstructed, a single loop simulated annealing algorithm(SAA) by using mutation and extending searching spaces was used to obtain optimal time sharing power scheme. Industrial practical results show that the whole system can greatly decrease the power consumption of EZP and increase the time sharing profits.

Calculation and Evaluation of Ecological Flow of Hydropower Station Based on Fuzzy Evaluation Model

The reasonable determination of ecological flow is of great significance for the efforts to promote the transformation of water ecological environmental protection from pollution management to synergistic management of water resources,water ecology and water environment,and to promote them in an integrated manner.This paper analyzed and calculated the ecological flow process of the Bangsha River diversion power station using the minimum ecological flow method,the annual spreading method,the improved annual spreading method,the NGPRP method,and the month-by-month frequency method,and evaluated the reasonableness of the process and results of the ecological flow calculations by using the fuzzy evaluation model established.The study showed that the minimum ecological flow rate determined by improving the coupling of the spreading method and the NGPRP method was the best,and the suitable ecological flow rate determined by the month-by-month frequency method was the best;the minimum ecological flow rate of the Bangsha River diversion power station was at 0.43-4.21 m 3/s,and the suitable ecological flow rate was at 0.56-4.94 m 3/s,and the trend of its change showed the trend of first increasing and then decreasing,and the trend of change from January to July showed the trend of first increasing and then decreasing.Its trend of change showed an increasing and then decreasing trend,from January to July showed a gradually increasing trend,from August to December showed a gradually decreasing trend.It aimed to provide a theoretical basis for the reasonable determination of the ecological flow of the river hydropower station.

Fuzzy optimization of pneumatic half-floating slide ways

Dynamic modeling was carried on by combining the dynamic of machinery with composite triology, and the critical condition in which the ways would not produce composite-friction self-excited vibration was obtained. The movement regularity and characteristic of the airflow in exhaust gas slit were analyzed, and the relationship between pressure lost and geometry parameters of exhaust gas slit was obtained. A dynamic model and a mathematical model were established for pneumatic half-floating slide ways by combining the dynamics of machinery with hydrokinetics. The objective function for the optimization of slide ways was established based on the fuzzy optimization theory. The membership function of fuzzy constraint was deduced, the fuzzy constraint limit was established by amplification coefficient method, and the optimal value was resolved by the multilevel fuzzy comprehensive evaluation method. By combining the internal penalty function method with the variable metric method, the fuzzy optimization design program of ways was designed based on the Matlab platform. The validation was carried on by an example, and ideal results of fuzzy optimization design of slide ways were obtained.

Neural Network Based Multi-level Fuzzy Evaluation Model for Mechanical Kinematic Scheme

To implement a quantificational evaluation for mechanical kinematic scheme more effectively,a multi-level and multi-objective evaluation model is presented using neural network and fuzzy theory. Firstly,the structure of evaluation model is constructed according to evaluation indicator system. Then evaluation samples are generated and provided to train this model. Thus it can reflect the relation between attributive value and evaluation result,as well as the weight of evaluation indicator. Once evaluation indicators of each candidate are fuzzily quantified and fed into the trained network model,the corresponding evaluation result is outputted and the best alternative can be selected. Under this model,expert knowledge can be effectively acquired and expressed,and the quantificational evaluation can be implemented for kinematic scheme with multi-level evaluation indicator system. Several key problems on this model are discussed and an illustration has demonstrated that this model is feasible and can be regarded as a new idea for solving kinematic scheme evaluation.

Reliability Growth Modeling and Optimal Release Policy Under Fuzzy Environment of an N-version Programming System Incorporating the Effect of Fault Removal Efficiency

Failure of a safety critical system can lead to big losses. Very high software reliability is required for automating the working of systems such as aircraft controller and nuclear reactor controller software systems. Fault-tolerant softwares are used to increase the overall reliability of software systems. Fault tolerance is achieved using the fault-tolerant schemes such as fault recovery （recovery block scheme）, fault masking （N-version programming （NVP）） or a combination of both （Hybrid scheme）. These softwares incorporate the ability of system survival even on a failure. Many researchers in the field of software engineering have done excellent work to study the reliability of fault-tolerant systems. Most of them consider the stable system reliability. Few attempts have been made in reliability modeling to study the reliability growth for an NVP system. Recently, a model was proposed to analyze the reliability growth of an NVP system incorporating the effect of fault removal efficiency. In this model, a proportion of the number of failures is assumed to be a measure of fault generation while an appropriate measure of fault generation should be the proportion of faults removed. In this paper, we first propose a testing efficiency model incorporating the effect of imperfect fault debugging and error generation. Using this model, a software reliability growth model （SRGM） is developed to model the reliability growth of an NVP system. The proposed model is useful for practical applications and can provide the measures of debugging effectiveness and additional workload or skilled professional required. It is very important for a developer to determine the optimal release time of the software to improve its performance in terms of competition and cost. In this paper, we also formulate the optimal software release time problem for a 3VP system under fuzzy environment and discuss a the fuzzy optimization technique for solving the problem with a numerical illustration.

Method to Improve Airborne Pollution Forecasting by Using Ant Colony Optimization and Neuro-Fuzzy Algorithms

This contribution shows the feasibility of improving the modeling of the non-linear behavior of airborne pollution in large cities. In previous works, models have been constructed using many machine learning algorithms. However, many of them do not work for all the pollutants, or are not consistent or robust for all cities. In this paper, an improved algorithm is proposed using Ant Colony Optimization (ACO) employing models created by a neuro-fuzzy system. This method results in a reduction of prediction error, which results in a more reliable prediction models obtained.

Fast generation method of fuzzy rules and its application to flux optimization in process of matter converting

A fast generation method of fuzzy rules for flux optimization decision-making was proposed in order to extract the linguistic knowledge from numerical data in the process of matter converting. The fuzzy if-then rules with consequent real number were extracted from numerical data, and a linguistic representation method for deriving linguistic rules from fuzzy if-then rules with consequent real numbers was developed. The linguistic representation consisted of two linguistic variables with the degree of certainty and the storage structure of rule base was described. The simulation results show that the method involves neither the time-consuming iterative learning procedure nor the complicated rule generation mechanisms, and can approximate complex system. The method was applied to determine the flux amount of copper converting furnace in the process of matter converting. The real result shows that the mass fraction of Cu in slag is reduced by 0.5%.

Fuzzy Optimal Control Design for Ship Steering

This paper presents a method to design a control scheme for nonlinear systems using fuzzy optimal control.In the design process,the nonlinear system is first converted into local subsystems using sector non linearity approach of Takagi Sugeno(T S)fuzzy modeling.For each local subsystem,an optimal control is designed.Then,the parameters of local controllers are defuzzified to construct a global optimal controller.To prove the effectiveness of this control scheme,simulations are performed using the mathematical model of Esso Osaka tanker ship for set point regulation with and without disturbance and reference tracking.In addition,the simulation results are compared with that of a PID controller for further verification and validation.It has been shown that the proposed optimal controller can be used for the nonlinear ship steering with good rise time,zero steady state error and fast settling time.

Optimization Agricultural Supply Chain:A Case Study of Fertilizer Supplier Selection

The 21st century is associated with the IndustrialRevolution 4.0 and the organic agriculture trend,making the utilization of high-quality fertilizers,abundant nutritional content,economical,and no affect to environment pollution.According to the new concept,clean agricultural production and organic agricultural products are not allowed to excessively use synthetic chemicals such as chemical fertilizers,and plant protection drugs,but priority is to use manure,organic fertilizers,and natural mineral fertilizers.Fertilizer must meet the balanced nutritional requirements of crops,maintain,and improve the fertility of the ground,protect the surrounding ecosystem,and leave harmful effects in agricultural products,products with high quality,safe for users and high economic efficiency for producers.To achieve the above goal,the selection of a fertilizer supplier is an important decision,supporting the supply chain’s sustainable development,fertilizer supplier selection is a multicriteria decision making model,the decision maker must assess all qualitative and quantitative factors.In this paper,the author proposed an integer decision making model including Fuzzy Analytic Hierarchy Process(FAHP)and Complex Proportional Assessment of Alternatives(COPRAS)for fertilizer supplier selection.The weightings of the criteria are calculated by using FAHP,COPRAS is then applied for ranking some potential fertilizer suppliers.The efficiency of the proposed models is proved by a case study conducted in a farm located in the south of Vietnam.This research is the first fertilizer supplier evaluation and se-lection model in Vietnam by interviewing experts and reviewing the literature.Re-search result is to provide a case study on evaluating supplier in agricultural supply chain utilizing the model proposed by the combination of FAHP and COPRAS models.

Ideal Drift Response Curve for Robust Optimal Damper Design for Elastic-Plastic MDOF Structures under Multi-Level Earthquakes Dedicated to Professor Karl S.Pister for his 95th birthday

A new method of robust damper design is presented for elastic-plastic multi-degree-of-freedom(MDOF)building structures under multi-level ground motions(GMs).This method realizes a design that is effective for various levels of GMs.The robustness of a design is measured by an incremental dynamic analysis(IDA)curve and an ideal drift response curve(IDRC).The IDRC is a plot of the optimized maximum deformation under a constraint on the total damper quantity vs.the design level of the GMs.The total damper quantity corresponds to the total cost of the added dampers.First,a problem of generation of IDRCs is stated.Then,its solution algorithm,which consists of the sensitivity-based algorithm(SBA)and a local search method,is proposed.In the application of the SBA,the passive added dampers are removed sequentially under the specified-level GMs.On the other hand,the proposed local search method can search the optimal solutions for a constant total damper quantity under GMs’increased levels.In this way,combining these two algorithms enables the comprehensive search of the optimal solutions for various conditions of the status of the GMs and the total damper quantity.The influence of selecting the type of added dampers(oil,hysteretic,and so on)and the selection of the input GMs on the IDRCs are investigated.Finally,a robust optimal design problem is formulated,and a simple local search-based algorithm is proposed.A simple index using the IDRC and the IDA curve of the model is used as the objective function.It is demonstrated that the proposed algorithm works well in spite of its simplicity.

Technical-economical optimization of horizontal axis wind turbines by means of the genetic algorithm

Wind turbine design is a trade-off between its potentially generated energy and manufacturing cost represented by the area of turbine surface in this research, and both factors are highly influenced by a number of design parameters. In this research, first, a weighted sum of these factors, with a negative weight for power, is assumed as the performance function to be minimized. Then, blade element modeling was performed for class NACA turbines to estimate the generated power based on the effective wind velocity in the area. As a novelty, a new algorithm based on fuzzy logic was proposed to determine the effective wind velocity by using the history of wind velocity in the area. The wind velocity, therefore, the generated power by a wind turbine, is largely dependent on its operation area. In the end, the genetic algorithm with decimal numeric genes was employed to determine the optimal design parameters of the turbine based on the recorded data. This study resulted in a computer program which integrated calculations of fluid dynamics into the genetic algorithm to optimally determine an appropriate turbine (its geometric parameters). The implementation of the proposed method on two different regions ended up with the design of the blade NACA5413 for Manjil and the blade NACA4314 for Semnan, both in Iran.

Generalized Algorithms of Discrete Optimization and Their Power Engineering Applications

Generalized algorithms for solving problems of discrete, integer, and Boolean programming are discussed. These algorithms are associated with the method of normalized functions and are based on a combination of formal and heuristic procedures. This allows one to obtain quasi-optimal solutions after a small number of steps, overcoming the NP-completeness of discrete optimization problems. Questions of constructing so-called “duplicate” algorithms are considered to improve the quality of discrete problem solutions. An approach to solving discrete problems with fuzzy coefficients in objective functions and constraints on the basis of modifying the generalized algorithms is considered. Questions of applying the generalized algorithms to solve multicriteria discrete problems are also discussed. The results of the paper are of a universal character and can be applied to the design, planning, operation, and control of systems and processes of different purposes. The results of the paper are already being used to solve power engineering problems.

Cat Swarm with Fuzzy Cognitive Maps for Automated Soil Classification

Accurate soil prediction is a vital parameter involved to decide appro-priate crop,which is commonly carried out by the farmers.Designing an auto-mated soil prediction tool helps to considerably improve the efficacy of the farmers.At the same time,fuzzy logic(FL)approaches can be used for the design of predictive models,particularly,Fuzzy Cognitive Maps(FCMs)have involved the concept of uncertainty representation and cognitive mapping.In other words,the FCM is an integration of the recurrent neural network(RNN)and FL involved in the knowledge engineering phase.In this aspect,this paper introduces effective fuzzy cognitive maps with cat swarm optimization for automated soil classifica-tion(FCMCSO-ASC)technique.The goal of the FCMCSO-ASC technique is to identify and categorize seven different types of soil.To accomplish this,the FCMCSO-ASC technique incorporates local diagonal extrema pattern(LDEP)as a feature extractor for producing a collection of feature vectors.In addition,the FCMCSO model is applied for soil classification and the weight values of the FCM model are optimally adjusted by the use of CSO algorithm.For exam-ining the enhanced soil classification outcomes of the FCMCSO-ASC technique,a series of simulations were carried out on benchmark dataset and the experimen-tal outcomes reported the enhanced performance of the FCMCSO-ASC technique over the recent techniques with maximum accuracy of 96.84%.

基于预瞄模型的农机路径跟踪模糊PID控制方法

为提高农机路径跟踪控制性能,提出一种基于预瞄模型的模糊PID控制方法。以农机预瞄模型中的预瞄航向角偏差为输入、前轮转角为输出构建农机路径跟踪PID控制律,再将预瞄航向角偏差作为航向角变化量的目标值引入农机运动学模型,得到能够适应速度变化的前轮转角基础值,并替换PID控制律中的比例部分,最后使用模糊控制实时动态调整控制参数。CarSim与Simulink联合仿真分析表明,当初始横向偏差3 m时,在作业速度1.5、2.0和3.0 m/s情况下,农机上线时间分别为10.85、8.35和5.15s。与传统预瞄控制相比,该方法在确保跟踪精度的同时能够显著提高系统的响应速度。

基于RCMFME和AO-ELM的齿轮箱损伤识别策略

针对模糊熵只考虑信号的局部特征而忽略信号的全局特征,导致齿轮箱故障识别的准确率不佳的问题,提出了一种基于精细复合多尺度模糊测度熵(RCMFME)、天鹰优化器(AO)优化极限学习机(ELM)的齿轮箱故障诊断方法。首先,在精细复合多尺度模糊熵的基础上,对矢量的构造方式进行了改进,提出了能够同时考虑时间序列局部特征和全局特征的RCMFME方法;随后,利用RCMFME指标提取了齿轮箱振动信号的熵值,组建了故障特征向量;接着,利用AO算法对极限学习机的参数进行了自适应搜索,生成了参数最优的多类别分类器;最后,将训练样本的故障特征向量输入至AO-ELM分类模型中进行了模型训练,以构造性能最优的分类器,并实现了对齿轮箱测试样本的故障识别目的;利用两种齿轮箱振动数据集进行了实验,在识别准确率和识别稳定性方面,与相关的特征提取方法进行了对比。研究结果表明:采用基于RCMFME和AO-ELM的故障诊断方法能够分别取得100%和98%的分类准确率,平均识别准确率分别达到了100%和98%,优于精细复合多尺度全局模糊熵(RCMGFE)、精细复合多尺度模糊熵(RCMFE)、精细复合多尺度样本熵(RCMSE)。该方法具有显著的应用潜力。

基于Fuzzy-AHP的采矿方法优选辅助系统开发与应用

采矿方法选择是矿山生产设计中的重要环节,如何便捷地优选采矿方法是矿山设计人员一直关心的问题。将采矿方法优选视为一个多目标模糊优化问题,运用模糊综合理论与层次分析法,建立了采矿方法优选的Fuzzy-AHP模型。然后,运用VB编程,开发了一套具有数据存储功能、运算便捷、操作简单和可视化程度高的计算机辅助优选系统。最后应用该系统对某硫铁矿采矿方法进行了优选,取得了良好的工程效果。实践表明,该系统为矿山采矿方法优选提供了一套可靠性高的辅助决策工具。