Добірка наукової літератури з теми "Fuzzy local weights"

An image fusion technique for magnetic resonance imaging (MRI) and positron emission tomography (PET) using local features and fuzzy logic is presented. The aim of proposed technique is to maximally combine useful information present in MRI and PET images. Image local features are extracted and combined with fuzzy logic to compute weights for each pixel. Simulation results show that the proposed scheme produces significantly better results compared to state-of-art schemes.

This paper presents a real-time path planning approach for controlling the motion of space-based robots. The algorithm can plan three-dimensional trajectories for agents in a complex environment which includes numerous static and dynamic obstacles, path constraints, and/or performance constraints. This approach is extended based on the dynamic window approach (DWA). As the classic reactive method for obstacle avoidance, DWA uses an optimized function to select the best motion command. The original DWA optimization function consists of three weight terms. Changing the weights of these terms will change the behavior of the algorithm. In this paper, to improve the evaluation ability of the optimization function and the robot’s ability to adapt to the environment, a new optimization function is designed and combined with fuzzy logic to adjust the weights of each parameter of the optimization function. Given that DWA has the defect of local minima, which makes the robot hard to escape U-shaped obstacles, a dual dynamic window method and local goals are adopted in this article to help the robot escape local minima. By comparison, the proposed method is superior to traditional DWA and fuzzy DWA (F_DWA) in terms of computational efficiency, smoothness and security.

Brick making contributes significantly to the of supply materials for the building industry. The majority of brick production sectors, especially in developing countries, employ polluting and energy-inefficient technologies. Due to the increasing pressures on manufacturing firms to improve economic performance and growing environmental protection issues, sustainable and clean production is the main concern for brick makers. This paper considers the technological, economic, environmental, social, and energy-oriented criteria to select the optimal brick production technologies. Therefore, technology selection is viewed as a multi-criteria group decision-making (MCGDM) problem. This research proposes a novel hybrid fuzzy MCGDM (HFMCGDM) model to tackle the problem. In this respect, first of all, the modified triangular fuzzy pair-wise comparison (MTFPC) method is proposed to compute the local weights of criteria and sub-criteria. Then, a fuzzy DEMATEL (FDEMATEL) method is presented to calculate the interdependencies between and within the criteria. Moreover, the integration of MTFPC and FDEMATEL methods is applied to calculate the global criteria weights. Afterward, a novel method is proposed to determine the experts’ weight. Considering the last aggregation approach to diminish data loss, a new version of a fuzzy TOPSIS method is proposed to find the local and global priorities of the candidates. Then, a case study is given to demonstrate the applicability and superiority of the proposed methodology. To get a deeper view about considering kilns, energy and environmental performance of which has been investigated. Moreover, a comparative analysis is presented to illuminate the merits of the proposed methodology. Eventually, a sensitivity analysis is conducted to peruse the influence of criteria weights on ranking order.

The construction project management (CPM) is very important and large segment of entire project manage­ment (PM). Realisation of construction projects is usually long term process which requests significant financial, mate­rial, human and other resources to fulfil contracted obligations and achieve a good quality of works. Therefore, making good decisions with the satisfaction of various criteria is one of the main conditions to achieve planed business objec­tives and finish the project in contracted time with good quality. This paper proposes a new procedure for determination of the weights of criteria and alternatives in the Fuzzy analytic hierarchy process (FAHP) with trapezoidal fuzzy number using a new method for finding eigenvelues and eigenvectors of the criteria and alternatives, which is based on expected values of the fuzzy numbers and their products. Local and global fuzzy weights of the alternatives are determined using linear programming. In the paper a formula for ranking fuzzy numbers by reduced generalized fuzzy mean is also pro­posed, since ranking by the coefficient of variation is not always reliable. In the presented case study, applying proposed method, from imprecise input data are obtained enough accurate and useful results for rational ranking of alternatives related to the project realization.

Because the traditional linear vectorization methods have some shortcomings including processing data slowly, being sensitive to noises and being easy to be distorted. Fuzzy rules and its inference mechanism are the assurance of achieving feature fusion. However, the self-learning function of FNN could train its weights; it is difficult to optimize fuzzy rules. Besides, the common FNN training algorithms have low constringency speed and are liable to run into the local optimization.PSO algorithm has high convergence speed and it is simpler on the operation and is more potential on optimizing FNN. Thus, PSO algorithm could be adapted to train FNN weights, and prune the redundancy links, optimize fuzzy rules base. In the paper we present an improving immune genetic algorithm based on chaos theory. The over-spread character and randomness of chaos can be used to initialize population and improve the searching speed, and the initial value sensitivity of chaos can be used to enlarge the searching space. To avoid the local optimization, the algorithm renews population and enhances the diversity of population by using density calculation of immune theory and adjusting new chaos sequence.

This study proposes the use of consistent fuzzy preference relations to evaluate the structure of hotel sustainable business model (HSBM) dimensions and the corresponding hierarchy of evaluation indicators, and predict the overall probability of success. As fuzzy preference relations require, a group of hotel professionals in Taiwan was asked to process pairwise comparisons using linguistic variables to determine the weights of dimensions and indicators. According to the results, finances were found to be the most important dimension, followed by human capital. The number of local cultural events in the hotel was identified as the most important indicator. The predictive values revealed the possibility for successful HSBM implementation, shedding light on the vision of sustainability for the hotel industry. The results of the present study contribute to the literature on sustainability by determining the importance and weights of dimensions and indicators for hotel business models, providing an example of the use of this strategic tool in generating and modifying sustainable business models for the hotel industry.

In this paper, a data clustering method named consensus fuzzy k-modes clustering is proposed to improve the performance of the clustering for the categorical data. At the same time, the coupling DNA-chain-hypergraph P system is constructed to realize the process of the clustering. This P system can prevent the clustering algorithm falling into the local optimum and realize the clustering process in implicit parallelism. The consensus fuzzy k-modes algorithm can combine the advantages of the fuzzy k-modes algorithm, weight fuzzy k-modes algorithm and genetic fuzzy k-modes algorithm. The fuzzy k-modes algorithm can realize the soft partition which is closer to reality, but treats all the variables equally. The weight fuzzy k-modes algorithm introduced the weight vector which strengthens the basic k-modes clustering by associating higher weights with features useful in analysis. These two methods are only improvements the k-modes algorithm itself. So, the genetic k-modes algorithm is proposed which used the genetic operations in the clustering process. In this paper, we examine these three kinds of k-modes algorithms and further introduce DNA genetic optimization operations in the final consensus process. Finally, we conduct experiments on the seven UCI datasets and compare the clustering results with another four categorical clustering algorithms. The experiment results and statistical test results show that our method can get better clustering results than the compared clustering algorithms, respectively.

Background: Congenital Heart Disease is one of the abnormalities in your heart's structure. To predict the tetralogy of fallot in a heart is a difficult task. Cluster is the collection of data objects, which are similar to one another within the same group and are different from the objects in the other clusters. To detect the edges, the clustering mechanism improve its accuracy by using segmentation, Colour space conversion of an image implemented in Fuzzy c-Means with Edge and Local Information. Objective: To predict the tetralogy of fallot in a heart, the clustering mechanism is used. Fuzzy c-Means with Edge and Local Information gives an accuracy to detect the edges of a fallot to identify the congential heart disease in an efficient way. Methods: One of the finest image clustering methods, called as Fuzzy c-Means with Edge and Local Information which will introduce the weights for a pixel value to increase the edge detection accuracy value. It will identify the pixel value within its local neighbor windows to improve the exactness. For evaluation , the Adjusted rand index metrics used to achieve the accurate measurement. Results: The cluster metrics Adjusted rand index and jaccard index are used to evaluate the Fuzzy c- Means with Edge and Local Information. It gives an accurate results to identify the edges. By evaluating the clustering technique, the Adjusted Rand index, jaccard index gives the accurate values of 0.2, 0.6363, and 0.8333 compared to other clustering methods. Conclusion: Tetralogy of fallot accurately identified and gives the better performance to detect the edges. And also it will be useful to identify more defects in various heart diseases in a accurate manner. Fuzzy c-Means with Edge and Local Information and Gray level Co-occurrence matrix are more promising than other Clustering Techniques.

The way of fault characteristic parameters fuzzy processing and optimizing the weights and thresholds of ANN by GA are studied. As a result, the convergent rate and convergent precision are greatly increased. Application to the fault diagnosis of a air blower system shows the new model overcomes the low learning rate and local optima of BP algorithm, and the fault diagnosis precision is effectively improved.

Робота виконана в Інституті прикладного системного аналізу Національного технічного університету України «Київський політехнічний інститут імені Ігоря Сікорського»
У дисертаційній роботі запропоновано методологію підтримки прийняття рішень, яка з використанням розробленого системного підходу дозволяє підвищити достовірність розв’язків в складних слабко структурованих системах на основі ієрархічних та мережевих моделей і включає нові та удосконалені методи: оцінювання і підвищення узгодженості матриць парних порівнянь загального виду залежно від властивостей цих матриць, розрахунку довірчих інтервалів для локальних ваг, розрахунку нечітких локальних ваг, гібридний метод розрахунку локальних та агрегованих ваг, метод комплексного оцінювання чутливості розв'язку та спосіб оцінювання реверсу рангів при використанні різних правил комбінування функцій довіри. Розроблено нові методики, засоби та система моделювання експертного оцінювання. Практичне значення одержаних результатів полягає у створенні інструментарію у вигляді системи підтримки прийняття рішень, який застосовано при розв'язанні практичних задач на замовлення міністерств і відомств України.
In the dissertation work, an important scientific and technical problem has been solved, which deals with development of mathematical and methodological support for increasing the reliability of solutions to decision analysis problems in complex weakly structured systems based on hierarchical and network models. The scientific novelty of the work is determined by the following theoretical and practical results obtained by author. Using proposed systematic approach, a new methodology of decision support is developed, which allows to increase the reliability of solutions of decision analysis problems in complex weakly structured systems on the basis of hierarchical and network models. This methodology includes the proposed and described below methods and techniques. A new method for evaluating and improving the consistency of expert judgements, which are given in a form of pairwise comparison matrix, is developed. Features of the method include an analysis of property of weak inconsistency, the presence of cycles in a pairwise comparison matrix and a search for the most inconsistent element of this matrix. The method can be applied to pairwise comparison matrices of various types, including multiplicative, additive, fuzzy and other. A Transitiv method for searching the most inconsistent elements of the matrix is proposed. A method of flows for finding the most inconsistent element of the matrix is improved by taking into account the input flow. The simulation shows that the developed Transitiv method and the method of flows are more efficient than existing methods. Usage of the proposed method of consistency evaluating and improving allows to obtain pairwise comparison matrices of acceptable quality for all elements of the model and these matrices can be used further to find local weights of model’s elements. A new method for calculating confidence intervals of local weights is developed, which, unlike others, takes into account the uncertainty of scale, expert's personal qualities such as optimism and pessimism, and does not require comparison of groups of elements with the frame. The method is based on notions of the Dempster-Schafer theory of evidence and results of computer simulation of expert's judgments. An uncertainty index of expert judgments is proposed, assuming that this uncertainty is caused by above factors. An improved method for calculating fuzzy local weights on basis of fuzzy pairwise comparison matrix is proposed, which differs from others in estimating and increasing the consistency of the matrix and taking into account properties of weak and strong order preservation on a set of calculated fuzzy weights. This method, unlike existing ones, makes it possible to determine the weak inconsistency of fuzzy matrix, to assess the acceptability of inconsistency level of fuzzy matrix for reliable local weights calculation, and to find the most inconsistent elements of the matrix using methods developed for crisp matrices. A hybrid method for calculating aggregated weights of hierarchical model elements with interdependent decision criteria has been improved, when input data for evaluation are fuzzy expert judgments. Improvement consists in using the developed more effective methods for assessment and increasing of crisp and fuzzy expert judgements consistency. A method for complex sensitivity analysis of results has been improved by taking into account sensitivity analysis of local rankings of model’s elements. In the developed method for estimating local sensitivity, intervals and indices of stability of pairwise comparison matrix elements are calculated, which retain the best decision alternative and all ranking of alternatives. Resulting stability intervals allow to find critical elements of the problem that require more careful analysis. A new technique for estimating the rank reversal is suggested, which can appear after applying combination rules of confidence functions for model’s elements. Using this technique, the Dempster, Yager, Zhang, Dubois and Prada and other combination rules were examined. Cases and features of rank reversals appearance in these rules were revealed. New techniques and tools for modeling a process of decision alternatives evaluation by an expert of high competence, expert-optimist and expert-pessimist while performing pairwise comparisons are developed. Using these techniques and tools, efficiency of proposed methods has been proved. A decision support system has been constructed on basis of proposed methods and techniques. This system has been used to solve several practical problems. Within the work with the Ministry of Education and Science of Ukraine, critical technologies of the Ukrainian energy industry were assessed, priorities of technologies were calculated and aggregated according to hierarchical model of criteria, and on their basis the most priority technologies for implementation were identified. In the course of research work together with the Institute of Space Research, directions of the expedient use of space information for remote sensing of the Earth for geoinformation systems were evaluated and the relative demand for the space information in the national economy of Ukraine was determined. On order of the Kyiv City State Administration, social problems of the Kyiv city were estimated in terms of benefits, costs, opportunities and risks, followed by selection of priority activities for implementation and evaluation of scenarios of the transport system development. Results of the dissertation work have been introduced into the educational process of the department "Mathematical methods of system analysis" of Institute for applied system analysis of National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”.
В диссертационной работе предложена методология поддержки принятия решений, которая с использованием разработанного системного подхода позволяет повысить достоверность решений в сложных слабо структурированных системах на основе иерархических и сетевых моделей и включает новые и усовершенствованные методы: оценки и повышения согласованности матриц парных сравнений общего вида в зависимости от свойств этих матриц, расчета доверительных интервалов для локальных весов, расчета нечетких локальных весов, гибридный метод расчета локальных и агрегированных весов, метод комплексной оценки чувствительности решения, а также способ оценки реверса рангов при использовании различных правил комбинирования функций доверия. Разработаны новые методики, средства и система моделирования экспертного оценивания. Практическое значение полученных результатов заключается в создании инструментария в виде системы поддержки принятия решений, который применен при решении практических задач по заказу министерств и ведомств Украины.

Робота виконана в Інституті прикладного системного аналізу Національного технічного університету України «Київський політехнічний інститут імені Ігоря Сікорського»
У дисертаційній роботі запропоновано методологію підтримки прийняття рішень, яка з використанням розробленого системного підходу дозволяє підвищити достовірність розв’язків в складних слабко структурованих системах на основі ієрархічних та мережевих моделей і включає нові та удосконалені методи: оцінювання і підвищення узгодженості матриць парних порівнянь загального виду залежно від властивостей цих матриць, розрахунку довірчих інтервалів для локальних ваг, розрахунку нечітких локальних ваг, гібридний метод розрахунку локальних та агрегованих ваг, метод комплексного оцінювання чутливості розв'язку та спосіб оцінювання реверсу рангів при використанні різних правил комбінування функцій довіри. Розроблено нові методики, засоби та система моделювання експертного оцінювання. Практичне значення одержаних результатів полягає у створенні інструментарію у вигляді системи підтримки прийняття рішень, який застосовано при розв'язанні практичних задач на замовлення міністерств і відомств України.

Power system scheduling is one of the most complex multi-objective scheduling problems, and a heuristic optimization method is designed for finding the OPF solution. Stochastic weight trade-off chaotic mutation-based non-dominated sorting particle swarm optimization algorithm can improve solution-search-capability by balancing between global best exploration and local best utilization through the stochastic weight and dynamic coefficient trade-off methods. This algorithm with chaotic mutation enhances diversity and search-capability, preventing premature convergence. Non-dominated sorting and crowding distance techniques efficiently provide the optimal Pareto front. Fuzzy function is used to select the local best compromise. Using a two-stage approach, the global best solution is selected from many local trials. The discussed approach can schedule the generators in the systems effectively, leading to savings in fuel cost, reduction in active power loss and betterment in voltage stability.

For the data assimilation algorithms, the observation error covariance plays an important role, because they control the weight that is given to the model forecast and to the observation in the solution, i.e., the analysis. In order to easily calculate, we often assume observation to be a diagonal matrix, however, the observation errors are correlated to the state and have a certain dependence on time, such as certain observing types which are remotely sensed. In this work, we obtain the time-dependent and correlated observation error by the method of observation error estimation in the data assimilation system. We combine the ensemble time-local H-infinity filter (EnTLHF) with an estimate of observation error covariance matrix, named ensemble time-local H-infinity filter with observation error covariance estimation (EnTLHF-R). In the experiment, a classical nonlinear Lorenz-96 model to evaluate the performance of new method is used. The results show that the robust filtering with observation error estimation is more accurate, more robust, and the filtering is more stable.

This chapter aims to measure the independence of the central banks of E7 economies. For this purpose, six different criteria are developed by considering similar studies in the literature. In the analysis process, fuzzy DEMATEL method is used to weight these criteria. On the other hand, both fuzzy TOPSIS and fuzzy VIKOR approaches are taken into consideration to rank E7 economies regarding the central bank independence. The findings show that preventing giving loan to the government is the most important factor while participating rate in the financial market has the weakest importance in the criteria set. It is recommended that central banks in E7 economies should not give loans to the government since this situation has a negative influence on the decisions of local and foreign investors. In addition to this issue, it is also concluded that Indonesia (A4) has the best performance in the E7 economies while Mexico (A5) has the worst performance in the central bank independency among the countries.

Abstract A novel neural-fuzzy system is developed by using the NURBS (Non-uniform Rational B-spline) curve as the membership functions in this paper. The neural-fuzzy system uses the fuzzy logic rule to estimate the output of the systems and uses the weight updating method in neural networks to adjust the rules. The NURBS interpolation method is proposed to construct adaptable membership functions for the proposed neural-fuzzy system. Since the shape of a NURBS curve is controlled by adjusting the control vertices and their weights, changing a control vertex and its weight will only affect the curve shape locally. This local control property reduces the number of iterations of learning in the system. Due to the adaptability of the NURBS membership function, the proposed system will need fewer fuzzy rules compared to existing systems but result in faster error convergence and better accuracy in system identification. The simulation results in Test Examples show that the presented NURBS neural-fuzzy system can identify complex systems with good accuracy and fast learning speed.

Abstract Reservoir engineering constitutes a major part of the studies regarding oil and gas exploration and production. Reservoir engineering has various duties, including conducting experiments, constructing appropriate models, characterization, and forecasting reservoir dynamics. However, traditional engineering approaches started to face challenges as the number of raw field data increases. It pushed the researchers to use more powerful tools for data classification, cleaning and preparing data to be used in models, which enhances a better data evaluation, thus making proper decisions. In addition, simultaneous simulations are sometimes performed, aiming to have optimization and sensitivity analysis during the history matching process. Multi-functional works are required to meet all these deficiencies. Upgrading conventional reservoir engineering approaches with CPUs, or more powerful computers are insufficient since it increases computational cost and is time-consuming. Machine learning techniques have been proposed as the best solution for strong learning capability and computational efficiency. Recently developed algorithms make it possible to handle a very large number of data with high accuracy. The most widely used machine learning approaches are: Artificial Neural Network (ANN), Support Vector Machines and Adaptive Neuro-Fuzzy Inference Systems. In this study, these approaches are introduced by providing their capability and limitations. After that, the study focuses on using machine learning techniques in unconventional reservoir engineering calculations: Reservoir characterization, PVT calculations and optimization of well completion. These processes are repeated until all the values reach to the output layer. Normally, one hidden layer is good enough for most problems and additional hidden layers usually does not improve the model performance, instead, it may create the risk for converging to a local minimum and make the model more complex. The most typical neural network is the forward feed network, often used for data classification. MLP has a learning function that minimizes a global error function, the least square method. It uses back propagation algorithm to update the weights, searching for local minima by performing a gradient descent (Figure 1). The learning rate is usually selected as less than one.

In order to eliminate the salt pepper and Gaussian mixed noise in X-ray weld image, the extreme value characteristics of salt and pepper noise are used to separate the mixed noise, and the non local mean filtering algorithm is used to denoise it. Because the smoothness of the exponential weighted kernel function is too large, it is easy to cause the image details fuzzy, so the cosine coefficient based on the function is adopted. An improved non local mean image denoising algorithm is designed by using weighted Gaussian kernel function. The experimental results show that the new algorithm reduces the noise and retains the details of the original image, and the peak signal-to-noise ratio is increased by 1.5 dB. An adaptive salt and pepper noise elimination algorithm is proposed, which can automatically adjust the filtering window to identify the noise probability. Firstly, the median filter is applied to the image, and the filtering results are compared with the pre filtering results to get the noise points. Then the weighted average of the middle three groups of data under each filtering window is used to estimate the image noise probability. Before filtering, the obvious noise points are removed by threshold method, and then the central pixel is estimated by the reciprocal square of the distance from the center pixel of the window. Finally, according to Takagi Sugeno (T-S) fuzzy rules, the output estimates of different models are fused by using noise probability. Experimental results show that the algorithm has the ability of automatic noise estimation and adaptive window adjustment. After filtering, the standard mean square deviation can be reduced by more than 20%, and the speed can be increased more than twice. In the enhancement part, a nonlinear image enhancement method is proposed, which can adjust the parameters adaptively and enhance the weld area automatically instead of the background area. The enhancement effect achieves the best personal visual effect. Compared with the traditional method, the enhancement effect is better and more in line with the needs of industrial field.