Research on Value Evaluation Method of Investment Project Based on Fuzzy Composite Real Options

Venture capital investments are characterized by high input,high yield,and high risk.Due to the complexity of the market environment,stage-by-stage investment is becoming increasingly important.Traditional evaluation methods like comparison,proportion,maturity,internal rate of return,scenario analysis,decision trees,and net present value cannot fully consider the uncertainty and stage characteristics of the project.The fuzzy real options method addresses this by combining real option theory,fuzzy number theory,and composite option theory to provide a more accurate and objective evaluation of Public-Private Partnership(PPP)projects.It effectively considers the interaction of options and the ambiguity of project parameters,making it a valuable tool for project evaluation in the context of venture capital investment.

基于Fuzzy Matter-Element模型的电力上市公司财务核心竞争能力评价

鉴于电力上市公司财务核心竞争能力评价的复杂性以及单项指标评价结果的不相容性,本文在模糊物元分析的基础上,将熵权的理论方法与海明贴近度的概念相结合,建立了电力上市公司财务核心竞争能力评价的模糊物元模型。把不同电力企业作为物元的事物,以它们的各项评价指标及相应的模糊量值构造模糊物元,通过计算与理想模糊物元之间的海明贴近度,实现对上市电力公司财务核心竞争能力的综合评价。

Evaluation model of venture investment based on fuzzy matter-element method of combined weight

An evaluation model of an international venture investment project on the basis of fuzzy matter-element and combined weight methods is introduced. First, the compound fuzzy matter-element of optimal subordinate degree is constructed on the principle of the bigger-more-optimal or the less-more-optimal depending on the actual evaluation indicators, and combined with standard fuzzy matter-element to form a difference-square fuzzy matter-element. Secondly, a combined weight is calculated by both information entropy and the expert grading method. Finally, the compound fuzzy matter-element of Euclidian approach degree by M(·,+)method is constituted and used to classify venture investment projects. Based on the model above, six venture investment projects in a company are evaluated, and the results show that the projects are all good, which is demonstrated by the good income of the projects. Therefore, the coincidence of evaluation results and actual operation status indicates that the model is of great value in practical application.

A method combining refined composite multiscale fuzzy entropy with PSO-SVM for roller bearing fault diagnosis

Combining refined composite multiscale fuzzy entropy(RCMFE)and support vector machine(SVM)with particle swarm optimization(PSO)for diagnosing roller bearing faults is proposed in this paper.Compared with refined composite multiscale sample entropy(RCMSE)and multiscale fuzzy entropy(MFE),the smoothness of RCMFE is superior to that of those models.The corresponding comparison of smoothness and analysis of validity through decomposition accuracy are considered in the numerical experiments by considering the white and 1/f noise signals.Then RCMFE,RCMSE and MFE are developed to affect extraction by using different roller bearing vibration signals.Then the extracted RCMFE,RCMSE and MFE eigenvectors are regarded as the input of the PSO-SVM to diagnose the roller bearing fault.Finally,the results show that the smoothness of RCMFE is superior to that of RCMSE and MFE.Meanwhile,the fault classification accuracy is higher than that of RCMSE and MFE.

Identification of denatured and normal biological tissues based on compressed sensing and refined composite multi-scale fuzzy entropy during high intensity focused ultrasound treatment

In high intensity focused ultrasound(HIFU)treatment,it is crucial to accurately identify denatured and normal biological tissues.In this paper,a novel method based on compressed sensing(CS)and refined composite multi-scale fuzzy entropy(RCMFE)is proposed.First,CS is used to denoise the HIFU echo signals.Then the multi-scale fuzzy entropy(MFE)and RCMFE of the denoised HIFU echo signals are calculated.This study analyzed 90 cases of HIFU echo signals,including 45 cases in normal status and 45 cases in denatured status,and the results show that although both MFE and RCMFE can be used to identify denatured tissues,the intra-class distance of RCMFE on each scale factor is smaller than MFE,and the inter-class distance is larger than MFE.Compared with MFE,RCMFE can calculate the complexity of the signal more accurately and improve the stability,compactness,and separability.When RCMFE is selected as the characteristic parameter,the RCMFE difference between denatured and normal biological tissues is more evident than that of MFE,which helps doctors evaluate the treatment effect more accurately.When the scale factor is selected as 16,the best distinguishing effect can be obtained.

Adaptive fuzzy compensation based composite nonlinear feedback controller design for robot manipulators

In order to suppress the influence of uncertain factors on robot system and enable an uncertain robot system to track the reference input accurately,a strategy of combining composite nonlinear feedback(CNF)control and adaptive fuzzy control is studied,and a robot CNF controller based on adaptive fuzzy compensation is proposed.The key of this strategy is to use adaptive fuzzy control to approach the uncertainty of the system online,as the compensation term of the CNF controller,and make full use of the advantages of the two control methods to reduce the influence of uncertain factors on the performance of the system.The convergence of the closed-loop system is proved by feedback linearization and Lyapunov theory.The final simulation results confirm the effectiveness of this plan.

River Health Assessment Based on Fuzzy Matter-element Model

[Objective] The study aimed to assess the health state of rivers by using fuzzy matter-element model.[Method] Based on fuzzy matter-element analysis theory,the assessment model of river health was established,then a modified method to calculate the superior subordinate degree was put forward according to Hamming distance.Afterwards,a multi-level evaluation model,which contained the assessment indicators about hydrological features,ecological characteristics,environmental traits and service function,was set up based on this method above.Finally,the model was applied in the health assessment of Qinhuai River.[Result] The health state of Qinhuai River was at medium level.This assessment result was consistent with that of comprehensive index method,and it showed that the multi-level fuzzy matter-element model was effective in the assessment of river health.[Conclusion] The research provided an effective method to evaluate the state of river health.

Adaptive Neuro-Fuzzy Inference System for Prediction of Effective Thermal Conductivity of Polymer-Matrix Composites

In the present study, the adaptive neuro-fuzzy inference system (ANFIS) is developed for the prediction of effective thermal conductivity (ETC) of different fillers filled in polymer matrixes. The ANFIS uses a hybrid learning algorithm. The ANFIS is a class of adaptive networks that is functionally equivalent to fuzzy inference systems (FIS). The ANFIS is based on neuro-fuzzy model, trained with data collected from various sources of literature. ETC is predicted using ANFIS with volume fraction and thermal conductivities of fillers and matrixes as input parameters, respectively. The predicted results by ANFIS are in good agreements with experimental values. The predicted results also show the supremacy of ANFIS in comparison with other earlier developed models.

Optimizing selection of bridge raft based on fuzzy matter-element model and combination weighting

It＇ s a necessary selection to support the maneuver across Yangtze River by floating bridge constructed by portable steel bridge and civilian ships. It is a comprehensive index for the scheme of bridge raft, containing a variety of technical factors and uncertainties. The optimization is the selection in the constructing time, quantity of equipments and man power. Based on the calculation result of bridge rafts, an evaluating system is established, consisting of index of spacing between interior bays, raft length, truss numbers, operation difficulty and maximal bending stress. A fuzzy matter element model of optimizing selection of bridge rafts was built up by combining quantitative analysis with qualitative analysis. The method of combination weighting was used to calculate the value of weights index to reduce the subjective randomness. The sequence of schemes and the optimization resuh were gained finally based on euclid approach degree. The application result shows that it is simple and practical.

Keystroke Dynamics Based Authentication Using Possibilistic Renyi Entropy Features and Composite Fuzzy Classifier

This paper presents the formulation of the possibilistic Renyi entropy function from the Renyi entropy function using the framework of Hanman-Anirban entropy function. The new entropy function is used to derive the information set features from keystroke dynamics for the authentication of users. A new composite fuzzy classifier is also proposed based on Mamta-Hanman entropy function and applied on the Information Set based features. A comparison of the results of the proposed approach with those of Support Vector Machine and Random Forest classifier shows that the new classifier outperforms the other two.

A FUZZY MODEL FOR THE FAILURE OF COMPOSITE STRUCTURES

In this paper, the fuzzy theory is used to describe the uncertainty in failure definition of composite structures. The concept of structural failure level (SFL) is suggested and a method of evaluation is presented.

Endpoint forecasting on composite regeneration by coupling cerium-based additive and microwave for diesel particulate filter

Numerical simulation has been carried out to investigate the major factors affecting the time of composite regeneration due to coupling cerium-based additive and microwave for diesel particulate f3ilter(DPF). Effect on the composite regeneration time from various factors such as mass flow rate of exhaust gas, temperature of exhaust gas, oxygen concentration of exhaust gas, microwave power and amount of cerium-based additive are investigated. And a mathematical model based on fuzzy least squares support vector machines has been developed to forecast the endpoint of the composite regeneration. The results show that the relative error of endpoint forecasting model of composite regeneration is less than 3.5%, and the oxygen concentration of exhaust gas has the biggest effect on the endpoint of composite regeneration, followed by the mass flow rate of exhaust gas, the microwave power, the temperature of exhaust gas and the amount of cerium-based additive.

A fuzzy distinguishability on species composition similarities of pelagic fishes in different years in the southern Taiwan Strait

A fuzzy mathematical method is used to discriminate the similarities of pelagic fishes land- ed in the 9180 hauls by 10 light-seine information vessels in the southern Fujian waters from 1989 to 1998. The results indicate that the dominant species of the communities had an obvious alternation and the fuzzy adjacency of annual species composition varied between 0. 659 and 0. 923 with an average value of 0. 791. The fuzzy clustering analysis indicates that the annual fuzzy adjacency in general remains good although the species composition of the pelagic fishes has changed to a certain degree since 1992. This paper concludes that the community structure of pelagic fishes in the southern Taiwan Strait remains rel- atively stable and the state of fish stocks shows a good potentiality for a larger utilization.

A Fuzzy Directed Graph-Based QoS Model for Service Composition

Web service composition lets developers create applications on top of service-oriented computing and its native description, discovery, and communication capabilities. This paper mainly focuses on the QoS when the concrete composition structure is unknown. A QoS model of service composition is presented based on the fuzzy directed graph theory. According to the model, a recursive algorithm is also described for calculating such kind of QoS. And, the feasibility of this QoS model and the recursive algorithm is verified by a case study. The proposed approach enables customers to get a possible value of the QoS before they achieve the service.

Composite Control of Precalciner Exit Temperature in Cement Rotary Kiln

A composite control strategy for the precalciner exit temperature in cement kiln is introduced based on a mathematical model. In this model, the raw meal flow, coal powder flow and wind flow are taken as three input variables, the clinker fow and exit teperature of cement kiln are output variables, and other influencing factors are considered as disturbance. A composite control system is synthesied by integrating self learning PID, fuzzy and feedforward function into a combined controller, and the arithmetics for the self learning PID controller, fuzzy controller and feedforward controller are elaborated respectively. The control strategy has been realized by software in real practice at cement factory. Application results show that the composite control technology is superior to the general PID control in control effect, and is suitable to the industrial process control with slow parameter variation, nonlinearity and uncertainty.

Seasonal Short-Term Load Forecasting for Power Systems Based onModal Decomposition and Feature-FusionMulti-Algorithm Hybrid Neural NetworkModel

To enhance the refinement of load decomposition in power systems and fully leverage seasonal change information to further improve prediction performance,this paper proposes a seasonal short-termload combination prediction model based on modal decomposition and a feature-fusion multi-algorithm hybrid neural network model.Specifically,the characteristics of load components are analyzed for different seasons,and the corresponding models are established.First,the improved complete ensemble empirical modal decomposition with adaptive noise(ICEEMDAN)method is employed to decompose the system load for all four seasons,and the new sequence is obtained through reconstruction based on the refined composite multiscale fuzzy entropy of each decomposition component.Second,the correlation between different decomposition components and different features is measured through the max-relevance and min-redundancy method to filter out the subset of features with strong correlation and low redundancy.Finally,different components of the load in different seasons are predicted separately using a bidirectional long-short-term memory network model based on a Bayesian optimization algorithm,with a prediction resolution of 15 min,and the predicted values are accumulated to obtain the final results.According to the experimental findings,the proposed method can successfully balance prediction accuracy and prediction time while offering a higher level of prediction accuracy than the current prediction methods.The results demonstrate that the proposedmethod can effectively address the load power variation induced by seasonal differences in different regions.

Fuzzy-PID复合控制在水泥冷却过程中的应用

针对PID在水泥生产线冷却过程中实际应用所发现的问题,提出了将Fuzzy-PID复合控制算法应用于现场的控制思路,该算法将Fuzzy和PID两种控制方式的控制优点有机结合,根据多次现场实验得到的模糊切换隶属度函数在两种控制方式下自动切换,该控制既能在动态过程中快速调节,又能在稳态过程中准确调节。冷却过程控制软件采用VC++编写,通过在某水泥厂的实际应用表明了该方案的正确性和实用性。

应用Fuzzy集合理论测辨系统Fuzzy模型的新方法——Fuzzy推理合成法

本文基于Fuzzy集合理论,根据待辨识系统的量测数据,应用Fuzzy推理合成规则,提出建立系统Fuzzy模型的新方法,通过建立水泥回转窑热工过程Fuzzy模型,证实了该方法的可行性.

FUZZY-PI控制器在水温控制系统中的应用

针对水温控制系统存在非线性、时滞的问题,采用FUZZY—PI复合控制器对其进行控制,既利用了模糊控制适应能力强、动态特性好、抗干扰能力强的特点,又利用了PI控制可消除系统静差的特点,从而使控制系统的动态和静态品质都得到了提高,在水温控制实验中取得了较理想的效果．

一种Fuzzy推理合成的新算法

本文针对Ｆｕｚｚｙ控制问题中通常采用的Ｍａｍｄａｎｉ推理合成规则提出一种改进算法．与原算法相比，本文给出的算法具有推理结论更确定，满足一致性条件等优点。理论分析和数值结果表明，该算法是一种更为实用的Ｆｕｚｚｙ推理合成算法．