The reward function is used to evaluate the actions of the Agent. In traditional RL algorithms, the Agent can only obtain the positive and negative sparse reward function by reaching the target point or colliding with an obstacle. The model does not receive any feedback until it receives the ...
Although the above yielded the translation vector 𝑡𝐶1𝐶2tC2C1, 𝑡𝐶1𝐶2tC2C1 was a translation vector that lost scale. According to the Euclidean transformation distance preserving property, the baseline length of the 𝐶1C1 motion to 𝐶2C2 in a GNSS coordinate system and the Eu...
of the first stage are used for clustering again with small clusters merged and grouped. The TSC is based on the combination of the two stages, which has an accurate clustering effect and good scalability. The similarity and dissimilarity between data individuals are calculated using the Euclidean...
([27]). Consider a continuous function 𝑓(𝑥),𝑥∈Ω, for any 𝜀> 0, there exists an FLS (7) such that sup𝑥∈Ω|𝑓(𝑥)−Φ𝑇𝑌(𝑥)|⩽𝜀, (8) where weight vector Ω∈𝑅𝑁 is a compact set, ε is fuzzy approximate error with 𝜀→0 as 𝑙→∞. ...
where 𝒙∈ℝ𝑛 denotes the state vector and 𝒖∈ℝ𝑚 is the control input. 𝒇(𝒙)∈ℝ𝑛 and 𝒈(𝒙)∈ℝ𝑛×𝑚 indicate the dynamic drift and control gain matrix. 𝒇(𝒙) is assumed to be an uncertain smooth and nonlinear function, which can be decomposed as ...
The similarity and dissimilarity between data individuals are calculated using the Euclidean distance function [19]. After one cycle of clustering, the classifications with obvious features are separated, but the obtained clustering results are not detailed enough. In this paper, a cyclic two- step ...
For example, Gong [34] proposed a series of entropy measures and used them to determine the weight vector of attributes in decision making. Gurmani et al. [35] proposed basic operational rules as well as a new score function of LIVq-ROFSs, and finally, a VIKOR-based MAGDM method was ...
It is used to represent the covariance distance of data and is an effective method to calculate the similarity of two unknown sample sets. Different from Euclidean distance, it considers the relationship between various characteristics and is scale-independent. Therefore, it is used to eliminate the...
In the real world, there commonly exists types of multiple attribute decision-making (MADM) problems with partial attribute values and weights totally unknown. Symmetry among some attribute information that is already known and unknown, and symmetry betw
The elements of C are therefore a measure of similarity between the two images, and the translation of the peak from the origin indicates the shift between them. In the context of slip detection, this relationship can be interpreted as a slip vector between two similar tactile sensor arrays ...