K-quasi-additive fuzzy integrals of set-valued mappings

We first define the quasi-addition and quasi-multiplication operations by introducing the inductive operator, and then, in the K-quasi-additive fuzzy measure space, we establish the K-quasi-additive fuzzy integral of a generally measurable set-valued mapping. Applying the integral transformation theorem, some basic properties of the K-quasi-additive fuzzy integrals with respect to this kind of set-valued mapping are studied. Finally, the generalized monotone convergence theorems of this kind of fuzzy integrals are obtained.     

Generating Fuzzy Rule-based Systems from Examples Based on Robust Support Vector Machine

This paper firstly proposes a new support vector machine regression （SVR） with a robust loss function, and designs a gradient based algorithm for implementation of the SVR,then uses the SVR to extract fuzzy rules and designs fuzzy rule-based system. Simulations show that fuzzy rule-based system technique based on robust SVR achieves superior performance to the conventional fuzzy inference method, the proposed method provides satisfactory performance with excellent approximation and generalization property than the existing algorithm.     

A method for observing the three-dimensional morphologies of inclusions in steel

A method for observing the three-dimensional morphologies of inclusions by deeply eroding a steel sample with a kind of organic solution composed of bromine water, acetone, and HCl (volume ratio, 45:45:10) was introduced. Four different kinds of inclusions in ultra low carbon steel were compared by metallographic observation and erosion observation. The results show that the three-dimensional morphologies of different kinds of inclusions could be observed clearly and simply after erosion. The method is useful for the observation and analysis of inclusions made by deep erosion. It also provides a new way to control and remove inclusions based on the true morphologies of inclusions.     

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.     

Weighted Proximal Support Vector Machines： Robust Classification

Despite of its great efficiency for pattern classification, proximal support vector machines (PSVM), a new version of SVM proposed recently, is sensitive to noise and outliers. To overcome the drawback, this paper modifies PSVM by associating a weight value with each input data of PSVM. The distance between each data point and the center of corresponding class is used to calculate the weight value. In this way, the effect of noise is reduced. The experiments indicate that new SVM, weighted proximal support vector machine (WPSVM), is much more robust to noise than PSVM without loss of computationally attractive feature of PSVM.     

A fuzzy rule based genetic algorithm and its application in FMS

Most of the FMS (flexible manufacturing systems) problems belong to NP-hard (non-polynomial hard) problems. The facility layout problem and job-shop schedule problem are such examples. GA (genetic algorithm) is applied to get an optimal solution. However, traditional GAs are usually of low efficiency because of their early convergence. In order to overcome the shortcoming of the GA a fuzzy rule based GA is proposed, in which a fuzzy logical controller is introduced to adjust the value of crossover probability, mutation probability and crossover length. The HGA (hybrid genetic algorithm), which is integrated with a fuzzy logic controller, can avoid premature convergence, and improve the efficiency greatly. Finally, simulation results of the facility layout problem and job-shop schedule problem are given. The results show that the new genetic algorithm integrated with fuzzy logic controller is excellent in searching efficiency.     

A maximum entropy distribution for wave heights of non-linear sea waves

Based on the maximum entropy principle, a probability density function (PDF) for the zero-crossing wave height (H) of random waves is derived as the simple form fn(H)=αHγe－βHn (n is a selectable positive integer) through solving a variational problem subject to some quite general constraints. This PDF maximizes the information entropy of H, and its parameters α, γ and β are expressed explicitly in terms of the distribution moments m, m=1,2,…,n, so it is well competent for describing the distribution of H of nonlinear sea waves with large uncertainty, and its parameters can be simply determined from available data. Comparisons between the PDF with n=3 and n=4 and the observed distributions of H from wave records measured in the East China Sea and in a wind-wave tunnel show fairly satisfying agreements.     

Takagi - Sugeno Fuzzy PID Gain - Scheduling Controllers

This paper aims at Takagi - Sugeno (TS) fuzzy controllers as gain scheduling (GS) schemes of PID controllers. A TS fuzzy controller employs arbitrary input fuzzy sets, product or Zadeh fuzzy logic AND, TS fuzzy rules with linear consequent, and the generalized defuzzifler containing the popular centrold defuzzifler as a special case. We first establish the relationship between the TS fuzzy controller and the linear PID controller. The TS ftizzy controller is accurately a nonlinear PID controller with gains continuously changing with Its process output. Then we point out that the TS fuzzy controller is closely related to the traditional gain scheduler. The gains of the TS ftizzy controller are determined by three two - Input - one - output fuzzy systems with singleton output fuzzy sets. Finally, as a demonstration, a simple TS fuzzy controller employing two linear input fuzzy sets, Zadeh fuzzy logic AND, and the popular centrold defuzzifler is designed to be the gain scheduler for the PID controller. Com     

A maximum entropy distribution for wave heights of non-linear sea waves

Based on the maximum entropy principle,a probability density function (PDF) for the zero-crossing wave height (H) of random waves is derived as the simple form fn(H)=αHγe-βHn (n is a selectable positive integer) through solving a variational problem subject to some quite general constraints. This PDF maximizes the information entropy of H, and its parameters α, γ and β are expressed explicitly in terms of the distribution moments m, m=1,2,…,n, so it is well competent for describing the distribution of H of nonlinear sea waves with large uncertainty, and its parameters can be simply determined from available data. Comparisons between the PDF with n=3 and n=4 and the observed distributions of H from wave records measured in the East China Sea and in a wind-wave tunnel show fairly satisfying agreements.     

Dependent-Chance Programming Models for Capital Budgeting in Fuzzy Environments

Capital budgeting is concerned with maximizing the total net profit subject to budget constraints by selecting an appropriate combination of projects. This paper presents chance maximizing models for capital budgeting with fuzzy input data and multiple conflicting objectives. When the decision maker sets a prospective profit level and wants to maximize the chances of the total profit achieving the prospective profit level, a fuzzy dependent-chance programming model, a fuzzy multi-objective dependent-chance programming model, and a fuzzy goal dependent-chance programming model are used to formulate the fuzzy capital budgeting problem. A fuzzy simulation based genetic algorithm is used to solve these models. Numerical examples are provided to illustrate the effectiveness of the simulation-based genetic algorithm and the potential applications of these models.     

Identification Method of Urban Road Traffic Conditions in the Internet of Vehicles Environment

In accordance with the specific deployment way of infrastructure and data exchanging technology in the Internet of vehicles(IoV),the acquiring and calculating method for three basic traffic flow parameters in IoV scenarios,including traffic flow,speed and density,was researched.Considering the complexity of traffic flow and fuzziness of human thinking,fuzzy c-means clustering algorithm based on the genetic algorithm(GA-FCM) was adopted in soft classification of urban road traffic conditions.Genetic algorithm(GA) introduced into fuzzy clustering could avoid fuzzy c-means(FCM) algorithm converging to the local infinitesimal point,which made the cluster result more precise.By means of computer simulation,data exchanging environment in IoV was imitated,and then test data set was divided into four parts.The simulation indicates that the identification method is feasible and effective for urban road traffic conditions in IoV scenarios.     

On the Fixed Point Index and Fixed Point Theorems for Semi-closed 1-set-contraction Mapping

In this paper we define the fixed point index of semi-closed 1-set-contraction mappings by new way and we use the fixed point index to prove some new fixed point theorems of semi-closed 1-setcontraction mappings including theorems on multiple positive fixed points,which extend main results of [1,2,3,7]. This paper is a continuation of Li[12].     

Pedigree transmission disequilibrium test for quantitative traits in farm animals

General pedigrees are very common in farm animals,and the recent availability of large panels of SNPs in domestic species has given new momentum to the search for the mutations underlying variation in quantitative traits.In this paper,we proposed a new transmission disequilibrium test approach,called the pedigree transmission disequilibrium test,which deals with general pedigrees and quantitative traits in farm animals.Compared with the existing pedigree disequilibrium test (PDT) and general linear model-based method QTDT,our approach performed better with higher power and lower type I error,especially in scenarios where the quantitative trait locus (QTL) effect was small.We also investigated the application of our approach in selective genotyping design.Our simulation studies indicated that it was plausible to implement a selective genotyping strategy in the proposed pedigree transmission disequilibrium test.We found that our approach performed equally well or better when only some proportion of the individuals in the two tails were genotyped compared with its performance when all the individuals in the pedigree were genotyped.     

A new non-monotone fitness scaling for genetic algorithm

The properties of selection operators in the genetic algorithm (GA) are studied in detail. It is indicated that the selection of operations is significant for both improving the general fitness of a population and leading to the schema deceptiveness. The stochastic searching characteristics of GA are compared with those of heuristic methods. The influence of selection operators on the GA' s exploration and exploitation is discussed, and the performance of selection operators is evaluated with the premature convergence of the GA taken as an example based on One-Max function. In order to overcome the schema deceptiveness of the GA, a new type of fitness scaling, non monotone scaling, is advanced to enhance the evolutionary ability of a population. The effectiveness of the new scaling method is tested by a trap function and a needle-in-haystack (NiH) function.     

Research of Dynamic Competitive Learning in Neural Networks

Introduce a method of generation of new units within a cluster and a algorithm of generating new clusters. The model automatically builds up its dynamically growing in ternal representation structure during the learning process. Comparing model with other typical classification algorithm such as the Kohonen‘s self-organizing map. the model realizes a multilevel classification of the input pattern with an optional accuracy and gives a strong support possibility for the parallel computational main processor. The idea is suitable for the high-level storage of complex datas structures for object recognition.     

A New Algorithm for Feature Matching in Reverse Engineering

Feature recognition and surface reconstruction from point clouds are difficulties in reverse engineering. A new surface reconstruction algorithm for slicing point cloud was presented. The contours of slice were extracted. Then, the intersection of two adjacent curve segments in the contour was obtained and curves feature was extracted. Finally, adjacent section contours were matched directly with Fourier-Mellin curve matching method for feature extraction. An example of 3-D model reconstruction shows the reliability and application of the algorithm.     

High density methane inclusions in Puguang Gasfield: Discovery and a T-P genetic study

Based on measurement of homogenization temperature of inclusions and Raman spectral analysis, high density methane inclusions were discovered in the Triassic reservoirs of Puguang Gasfield. The methane inclusions show a homogenization temperature Th = -117.5― -118.1℃, a corresponding density of 0.3455―0.3477 g/cm3, and a Raman scatter peak v1 shift varying between 2911―2910 cm-1, which signifies a very high density of methane inclusions. The salt water inclusions paragenetic with methane inclusions show a homogenization temperature Th=170―180℃. Based on the composition of methane inclusions as determined by Raman spectra, PVTsim software was used to simulate the trapping pressure for high density methane inclusions in geologic history, and the trapping pressure was found to be as high as 153―160 MPa. Even though Puguang Gasfield is currently a gas pool of normal pressure, and the fluid pressure for the gas pool ranges between 56―65 MPa. However, data from this study indicates that remarkable overpressure may be generated at the stage of mass production of gas cracked from oils in Cretaceous, as high density methane inclusions constitute key evidence for overpressure in gas pool in geologic history. Meanwhile, discovery of small amounts of H2S, CO2 or heavy hydrocarbon in part of the high density methane inclusions indicates that the geochemical environment for trapping of inclusions may be related to formation of H2S. Therefore, the observation results can help to explore the thermochemical sulfate reduction （TSR） conditions for oil cracking and H2S formation.     

A Self-tuning Fuzzy Queue Management Algorithm for Congestion Control

This letter presents an effective self-tuning fuzzy queue management algorithm for congestion control.With the application of the algorithm, routers in IP network regulate its packet drop probability by a self-tuning fuzzy controller. The main advantage of the algorithm is that, with the parameter self-tuning mechanism, queue length can keep stable in a variety of network environments without the difficulty of parameter configuration. Simulations show that the algorithm is efficient, stable and outperforms the popular RED queue management algorithm significantly.     

NN adaptive pole placement method for sintering finish point control

A neural network model with a special structure, which is divided into linear and nonlinear parts, was proposed for identification of a nonlinear system. In this model, the nonlinear part of the object is treated as a measured disturbance, and is compensated by a feed forward method; an adaptive pole placement algorithm is used to control the linear part of the object. The simulation results show that the identification efficiency and accuracy are improved when the new controller is applied to sintering finish point control.