as in regular lattices. Communication is efficient because distances are orders of magnitude smaller than the system size. In real networks, the small world property is generally coupled with a high average clustering coefficient. The Watts–Strogatz model...
WFN are scale-free, the exponent being the fractal dimension. WFN exhibit the “small-world” property (i.e. slow (logarithmic) increase of the average shortest path with the network size) and large average clustering coefficient. Thus, the fractal dimension of weighted polymer networks is ...
Hence, for the above division of the finite, discrete representation pn of the continuous probability distribution p(x) one obtains the following grouping formula corresponding to the ordered (sequential) groups of histogramic points, (3.6.3)….<x1,α−1<x2,α−1,…<x1,α<x2,α,…<x1...
The maximum dissimilarity algorithm (a data-clustering algorithm) is used to separate the existing data sets in the training, validation, and testing groups to feed GP algorithm. Finally, by weighted combination, a new velocity formula with high accuracy and physical basis is proposed for submerged...
The proposed method along with TargetS does not perform well on an independent PDNA-52 dataset because TargetS has equipped the residues’ 3D coordinates contained in the PDB file to spatial clustering before it probes binding sites. Results of experiments show that our model is also compatible ...
Keywords: average wind power; interval forecasts; optimal subtractive clustering method; ANFIS; SSA; Model comparison 1. Introduction 1.1. Motivation Given the important environmental advantages of renewable energy sources, the installation of wind power plants has significantly increased in most ...
Because the error value of the ARIMA prediction in the future is the cumulative error, one-by-one, therefore, the value of the correction factor is increased gradually, hence Formula (8) is adopted so as to improve the prediction accuracy. 𝑦𝑡+𝑛̂=𝑦𝑡+𝑛×𝐷𝑡𝑒 𝑦...
In the formula, 𝛾γ is the residual value of the observed value and the forecasted value; 𝑐𝑘(𝑘=1,2,⋯)ck(k=1,2,⋯) is the observation value of the modeling data; 𝑋˜𝑡X˜t is the prediction value after the prediction residual correction; and 𝑋̂𝑡X^t and...