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In reinforcement learning, it is important to get nearly right answers early. Good prediction early can reduce the prediction error afterward and accelerate learning speed. We propose fuzzy Q-map, function approximation algorithm based on on-line fuzzy clustering in order to accelerate learning. Fuzzy Q-map can handle the uncertainty owing to the absence of environment model. Applying membership function...
This paper presents a new model to identify the criticality class of spare parts (SPs) based on fuzzy and grey theory. By using group-discussing and anonymous questionnaire methods, index set for the evaluation of criticality class of SPs are put forward. A new algorithm, which is integrated of modified Delphi, AHP, fuzzy comprehensive evaluation and grey relational analysis, is designed to convert...
This paper deals with new regression method of predicting fuzzy multivariable nonlinear regression models using triangular fuzzy numbers. The proposed method is achieved by implementing the locally weighted least squares support vector machine regression where the local weight is obtained from the positive distance metric between the test data and the training data. Two types of distance metrics for...
In this paper, by considering job shop dynamic scheduling problem in fuzzy environments, the description and modeling of the manufacturing systems is presented. A fuzzy dynamic scheduling strategy, in which compartmentalizing the period of time for rescheduling is based on computing triangular fuzzy number denoting the workpiece processing time, is proposed. For being solved, the job shop fuzzy dynamic...
The paper attempts to introduce a fundamental fuzzy concept to break the equivalent attitude of the input training set of SVM, and tries to give individual example in the set a different attitude. The attitude can stand for the influence that the example takes into account in the classification. In the paper, we present a method to refresh the attitude by assigning proper fuzzy value to the class...
Many authors considered the computational aspect of sup-min convolution when applied to weighted average operations. They used a computational algorithm based on a-cut representation of fuzzy sets, nonlinear programming implementation of the extension principle, and interval analysis. It is well known that TW (the weakest t-norm)-based addition and multiplication preserve the shape of L-R type fuzzy...
This paper is concerned with the fuzzy support vector classification in which the type of both the output of the training point and the value of the final fuzzy classification function is triangle fuzzy number. First, the fuzzy classification problem is formulated as a fuzzy chance constrained programming. Then we transform this programming into its equivalence quadratic programming. As a result,...
In this paper, we present a multi-constraint evolutionary algorithm based scheduler for fuzzy grid job scheduling. A chaotic genetic algorithm (CGA) is proposed to schedule jobs with uncertain operation time and flexible deadline on grid. The uncertainty is modeled by fuzzy set based execution time (FSET) model. Chaos is incorporated into standard genetic algorithm by logistic function, a simple equation...
There are many uncertain factors in job shop scheduling problems. However, those uncertainties are critical for the scheduling procedures. The imprecise processing times are modeled as triangular fuzzy numbers (TFNs) and the due dates are modeled as trapezium fuzzy numbers in this paper. A multi-objective genetic algorithm is proposed to solve fuzzy job shop scheduling problems, in which the objective...
Most current intrusion detection system employ signature-based methods that rely on labeled training data, however, in practice, this training data is typically expensive to produce. In contrast, unsupervised anomaly detection has great utility within the context of network intrusion detection system. Such a system can work without the need for massive sets of pre-labeled training data. Thus, with...
Target recognition based on high range resolution (HRR) polarized radar using support vector machines (SVMs) was studied in this paper. A fuzzy membership function was constructed based on SVM decision-making function in order to improve the performance of OAA and OAO classifiers for multi-class target, and HRR radar target recognition method using improved SVM was proposed: First, the polarized radar...
Cluster validity index is used to evaluate the clustering result yielded by the fuzzy clustering algorithm. In this paper, a new cluster validity index is proposed to determine the optimal fuzzy c-partition produced by the fuzzy c-means algorithm. The proposed index introduces two evaluation factors: distribution density and uncertainty. The first factor measures the extent of closeness or compactness...
Most car navigation systems estimate the car position from dead reckoning and the Global Positioning System (GPS). However, because of the unknown GPS noise, the estimated position has an undesirable error. To solve this problem, a map-matching method is introduced, which uses a digital road map to correct the position error. In this paper, based on analyzing the pure geometry and possibility statistics...
In order to raise Chinese pills' productive efficiency, a microwave resonator (MR) with a center hole is improved for measuring density and moisture content of Chinese pill material. When pill material passes through the center hole, its density and moisture will cause resonant frequency excursion (RFE) and microwave attenuation (MA). Under the same moisture, density is only related to MA. If RFE...
Because of the surprisingly increasing volume and semantically fuzzy nature of remote sensing images (RSIs), one of the main obstacles to realize efficient retrieval of the RSIs is the lack of effective sharing technologies and semantic description methods. In this paper, we present a fuzzy ontology and implement a prototype grid system named RSIsFGrid for semantic-based RSIs retrieval using fuzzy...
Building a trust and reputation mechanism in P2P system is very important. This paper presented a trust model of choosing trusted source peers in P2P system based on the fuzzy relation theory in fuzzy mathematics. The authors defined four types of fuzzy trust relations, and gave the ways of fuzzy global trust relation matrix computing, which used as the base of judging which source peers were trusted...
Fuzzy c-means (FCM) clustering algorithm has been widely used in automated image segmentation. However, the standard FCM algorithm is sensitive to noise because of taking no into account the gray and spatial information of pixel. The paper proposes an improved FCM algorithm for image segmentation. We use the degree of gray similarity and distribution statistics of the neighbor pixels to form a new...
This paper is to investigate a new unsupervised approach for the extracted objects based on synthetic aperture radar (SAR) image using improving fuzzy clustering method. The traditional fuzzy c-means clustering (FCM) is very sensitive to the initial value and the number of clusters. The accurate initial value and number of clusters are important parameters to get the accurate result in FCM. SAR image...
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