An improved method to determine basic probability assignment based on fuzzy mathematics

In D-S evidence theory, the determination of the basic probability assignment function (BPA) is the first and important step. However, the generation of BPA is still a problem to be solved. Based on the concepts in fuzzy mathematics, this paper proposes an improved BPA generation method. By introducing the value of the intersection point of membership function of different targets under the same index to describe the overlap degree of targets, the assignment of unknown items is optimized on this basis. This article applies it to target recognition of robot hands. The results show that the proposed method is more reliable and more accurate.

A new divergence measure of interval-valued pythagorean fuzzy sets and its application

As the extension of the Fuzzy sets (FSs) theory, the Interval-valued Pythagorean Fuzzy Sets (IVPFS) was introduced which play an important role in handling the uncertainty. The Pythagorean fuzzy sets (PFSs) proposed by Yager in 2013 can deal with more uncertain situations than intuitionistic fuzzy sets because of its larger range of describing the membership grades. How to measure the distance of Interval-valued Pythagorean fuzzy sets is still an open issue. Jensen–Shannon divergence is a useful distance measure in the probability distribution space. In order to efficiently deal with uncertainty in practical applications, this paper proposes a new divergence measure of Interval-valued Pythagorean fuzzy sets,which is based on the belief function in Dempster–Shafer evidence theory, and is called IVPFSDM distance. It describes the Interval-Valued Pythagorean fuzzy sets in the form of basic probability assignments (BPAs) and calculates the divergence of BPAs to get the divergence of IVPFSs, which is the step in establishing a link between the IVPFSs and BPAs. Since the proposed method combines the characters of belief function and divergence, it has a more powerful resolution than other existing methods.

Automatic Vehicle Route Prediction based on Multi-Sensor Fusion

To solve the problem of inaccurate results of vehicle routing prediction caused by a large number of uncertain information collected by different sensors in previous automatic vehicle route prediction algorithms, an automatic vehicle route prediction algorithm based on multi-sensor fusion is studied. The process of fusion of multi-sensor information based on the D-S evidence reasoning fusion algorithm is applied to automatic vehicle route prediction. According to the contribution of a longitudinal acceleration sensor and yaw angular velocity sensor detection information to the corresponding motion model, the basic probability assignment function of each vehicle motion model is obtained; the basic probability assignment function of each motion model is synthesized by using D-S evidence reasoning synthesis formula. The new probability allocation of each motion model is obtained under all evidence and then deduced according to the decision rules. Guided by the current optimal motion model, the optimal motion model at each time is used to accurately predict the vehicle movement route. The simulation results show that the prediction error of the algorithm is less than 4% in the process of 30 minutes of automatic vehicle route prediction.

An Improved K-Means Algorithm Based on Evidence Distance

The main influencing factors of the clustering effect of the k-means algorithm are the selection of the initial clustering center and the distance measurement between the sample points. The traditional k-mean algorithm uses Euclidean distance to measure the distance between sample points, thus it suffers from low differentiation of attributes between sample points and is prone to local optimal solutions. For this feature, this paper proposes an improved k-means algorithm based on evidence distance. Firstly, the attribute values of sample points are modelled as the basic probability assignment (BPA) of sample points. Then, the traditional Euclidean distance is replaced by the evidence distance for measuring the distance between sample points, and finally k-means clustering is carried out using UCI data. Experimental comparisons are made with the traditional k-means algorithm, the k-means algorithm based on the aggregation distance parameter, and the Gaussian mixture model. The experimental results show that the improved k-means algorithm based on evidence distance proposed in this paper has a better clustering effect and the convergence of the algorithm is also better.

A generalized TODIM-ELECTRE II method based on linguistic Z-numbers and Dempster–Shafer evidence theory with unknown weight information

How to effectively reflect the randomness and reliability of decision information under uncertain circumstances, and thereby improve the accuracy of decision-making in complex decision scenarios, has become a crucial topic in the field of uncertain decision-making. In this article, the loss –aversion behavior of decision-makers and the non-compensation between attributes are considered. Furthermore, a novel generalized TODIM-ELECTRE II method under the linguistic Z-numbers environment is proposed based on Dempster–Shafer evidence theory for multi-criteria group decision-making problems with unknown weight information. Firstly, the evaluation information and its reliability are provided simultaneously by employing linguistic Z-numbers, which have the ability to capture the arbitrariness and vagueness of natural verbal information. Then, the evaluation information is used to derive basic probability assignments in Dempster–Shafer evidence theory, and with the consideration of both inner and outer reliability, this article employed Dempster’s rule to fuse evaluations. Subsequently, a generalized TODIM-ELECTRE II method is conceived under the linguistic Z-numbers environment, which considers both compensatory effects between attributes and the bounded rationality of decision-makers. In addition, criteria weights are obtained by applying Deng entropy which has the ability to deal with uncertainty. Finally, an example of terminal wastewater solidification technology selection is offered to prove this framework’s availability and robustness. The predominance is also verified by a comparative analysis with several existing methods.

Data combination method in Remote Sensing tasks in case of conflicting information sources

Nowadays technologies of UAV-based Remote Sensing are used in different areas, such as: ecological monitoring, agriculture tasks, exploring for minerals, oil and gas, forest monitoring and warfare. Drones provide information more rapidly than piloted aerial vehicles and give images of a very high resolution, sufficiently low cost and high precision.Let’s note, that processing of conflicting information is the most important task in remote sensing. Dempster’s rule of data combination is widely used in solution of different remote sensing tasks, because it can processes incomplete and vague information. However, Dempster’s rule has some disadvantage, it can not deal with highly conflicted data. This rule of data combination yields wrong results, when bodies of evidence highly conflict with each other. That’s why it was proposed a data combination method in UAV-based Remote Sensing. This method has several important advantages: simple calculation and high accuracy. In this paper data combination method based on application of Jaccard coefficient and Dempster’s rule of combination is proposed. The described method can deal with conflicting sources of information. This data combination method based on application of evidence theory and Jaccard coefficient takes into consideration the associative relationship of the evidences and can efficiently handle highly conflicting sources of data (spectral bands).The frequency approach to determine basic probability assignment and formula to determine Jaccard coefficient are described in this paper too. Jaccard coefficient is defined as the size of the intersection divided by the size of the union of the sample sets. Jaccard coefficient measures similarity between finite sets. Some numerical examples of calculation of Jaccard coefficient and basic probability assignments are considered in this work too.This data combination method based on application of Jaccard coefficient and Dempster’s rule of combination can be applied in exploring for minerals, different agricultural, practical and ecological tasks.

A Novel Conflict Management Method Based on Uncertainty of Evidence and Reinforcement Learning for Multi-Sensor Information Fusion

Dempster–Shafer theory (DST), which is widely used in information fusion, can process uncertain information without prior information; however, when the evidence to combine is highly conflicting, it may lead to counter-intuitive results. Moreover, the existing methods are not strong enough to process real-time and online conflicting evidence. In order to solve the above problems, a novel information fusion method is proposed in this paper. The proposed method combines the uncertainty of evidence and reinforcement learning (RL). Specifically, we consider two uncertainty degrees: the uncertainty of the original basic probability assignment (BPA) and the uncertainty of its negation. Then, Deng entropy is used to measure the uncertainty of BPAs. Two uncertainty degrees are considered as the condition of measuring information quality. Then, the adaptive conflict processing is performed by RL and the combination two uncertainty degrees. The next step is to compute Dempster’s combination rule (DCR) to achieve multi-sensor information fusion. Finally, a decision scheme based on correlation coefficient is used to make the decision. The proposed method not only realizes adaptive conflict evidence management, but also improves the accuracy of multi-sensor information fusion and reduces information loss. Numerical examples verify the effectiveness of the proposed method.

A New Total Uncertainty Measure from A Perspective of Maximum Entropy Requirement

The Dempster-Shafer theory (DST) is an information fusion framework and widely used in many fields. However, the uncertainty measure of a basic probability assignment (BPA) is still an open issue in DST. There are many methods to quantify the uncertainty of BPAs. However, the existing methods have some limitations. In this paper, a new total uncertainty measure from a perspective of maximum entropy requirement is proposed. The proposed method can measure both dissonance and non-specificity in BPA, which includes two components. The first component is consistent with Yager’s dissonance measure. The second component is the non-specificity measurement with different functions. We also prove the desirable properties of the proposed method. Besides, numerical examples and applications are provided to illustrate the effectiveness of the proposed total uncertainty measure.