By Jaime Nava, Vladik Kreinovich

ISBN-10: 3662449544

ISBN-13: 9783662449547

ISBN-10: 3662449552

ISBN-13: 9783662449554

This booklet demonstrates the way to describe and examine a system's habit and extract the specified prediction and keep an eye on algorithms from this research. a standard prediction is predicated on watching related events long ago, understanding the results of those earlier events, and watching for that the long run end result of the present scenario can be just like those prior saw results. In mathematical phrases, similarity corresponds to symmetry, and similarity of results to invariance.

This e-book indicates how symmetries can be utilized in all periods of algorithmic difficulties of sciences and engineering: from research to prediction to manage. purposes hide chemistry, geosciences, clever regulate, neural networks, quantum physics, and thermal physics. in particular, it truly is proven how the method in keeping with symmetry and similarity can be utilized within the research of real-life platforms, within the algorithms of prediction, and within the algorithms of control.

**Read or Download Algorithmic Aspects of Analysis, Prediction, and Control in Science and Engineering: An Approach Based on Symmetry and Similarity PDF**

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**Extra info for Algorithmic Aspects of Analysis, Prediction, and Control in Science and Engineering: An Approach Based on Symmetry and Similarity**

**Example text**

X2n ,Y2n , . ). In other words, for every two tuples z1 = (X11 ,Y11 , . . , X1n ,Y1n , . ) and z2 = (X21 ,Y21 , . . , X2n ,Y2n , . ), once we define their sum component-wise def z1 + z2 = (X11 + X21 ,Y11 + Y21, . . , X1n + X2n,Y1n + X2n, . ), we get P(z1 + z2 ) = P(z1 ) + P(z2). 4 Why Feynman Path Integration 37 P(X1 ,Y1 , . . , Xn ,Yn , . ) = a1 · X1 + b1 ·Y1 + . . + an · Xn + bn ·Yn + . . (Let us recall that we operate on the physical level of rigor, where we ignore the difference between the finite sum and the limit (infinite) sum such as an integral.

For F(n) = ∑ f (i). In i=1 terms of the function F(n), the above question takes the following form: what is the best choice of the function F(n)? From a Discrete Problem to a Continuous Problem. The function F(x) is only defined for integer values x – if we use bits to measure the length of the longest repeated substring. , 1 bit corresponds to 1/8 of a byte, etc. If we use Kilobytes to describe the length, we can use even smaller fractions. In view of this possibility to use different units for measuring length, let us consider the values F(x) for arbitrary real lengths x.

Thus, it is desirable to describe computable approximations to K(s). Usual Approaches to Approximating Kolmogorov Complexity: Description and Limitations. At present, most algorithms for approximating K(s) use some loss-less compression technique to compress s, and take the length K(s) of the compression as the desired approximation. This approximation has limitations. For example, in contrast to K(s), where a small (one-bit) change in x cannot change K(s) much, a small change in s can lead to a drastic change in K(s).

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