We study structural, integral, differential, variational, and fractal properties of a function f whose argument $$ x={\Delta }_{\alpha_1{\alpha}_2\dots {\alpha}_n\dots}^{Q_s^{\ast }} is represented by polybasic s-symbol {Q}_s^{\ast } -representation (1 < s ∈ N) its corresponding value expressed as follows: f\left(x={\Delta }}\right)={a}^{\alpha_1{u}_1+{\alpha}_2{u}_2+\dots +{\alpha}_n{u}_n+\dot...

We introduce fractal Tsallis entropy and show that it satisfies Shannon–Khinchin axioms. Analogously to divergence (or relative entropy, according some authors), is defined properties of are studied. Within this framework, Lesche stability verified an example concerning the microcanonical ensemble given. generalize LMC complexity measure (LMC Lopez-Ruiz, Mancini Calbert), apply a two-level syst...

It has been demonstrated that higher order recurrent neural networks exhibit an underlying fractal attractor as an artifact of their dynamics. These fractal attractors o er a very e cent mechanism to encode visual memories in a neural substrate, since even a simple twelve weight network can encode a very large set of di erent images. The main problem in this memory model, which so far has remai...

Because of the great data of the hyperspectral image, it is not good for storage and transmission. To work out a high efficiency compression method for hyperspectral image is essential. Prediction encoding is easy to realize and has been studied widely in the hyperspectral image compression field. High compression ratio, resolution independence and fast decoding speed are the main advantages of...