Swarm intelligence is an innovative computational way to solving hard problems.
This discipline is inspired by the behavior of social insects such as fish
schools and bird flocks and colonies of ants, termites, bees and wasps. In general,
this is done by mimicking the behavior of the biological creatures within
their swarms and colonies.
The main features of the considered identification problem are that there is no an a priori separation of the variables into inputs and outputs and the approximation criterion, called misfit, does not depend on the model representation. The misfit is defined as the minimum of the l2-norm between the given time series and a time series that is consistent with the approximate model. The misfit is equal to zero if and only if the model is exact and the smaller the misfit is (by definition) the more accurate the model is. The considered model class consists of all linear time-invariant systems of bounded complexity and the complexity is specified by the number of inputs and the smallest number of lags in a difference equation representation. We present a Matlab function for approximate identification based on misfit minimization. Although the problem formulation is representation independent, we use input/state/output representations of the system in order
The toolbox solves a variety of approximate modeling problems for linear static models. The model can be parameterized in kernel, image, or input/output form and the approximation criterion, called misfit, is a weighted norm between the given data and data that is consistent with the model. There are three main classes of functions in the toolbox: transformation functions, misfit computation functions, and approximation functions. The approximation functions derive an approximate model from data, the misfit computation functions are used for validation and comparison of models, and the transformation functions are used for deriving one model representation from another.
KEYWORDS: Total least squares, generalized total least squares, software implementation.
an approach for capturing similarity between words that was concerned with the syntactic similarity of two strings. Today we are back to discuss another approach that is more concerned with the meaning of words. Semantic similarity is a confidence score that reflects the semantic relation between the meanings of two sentences. It is difficult to gain a high accuracy score because the exact semantic meanings are completely understood only in a particular context.
In the previous article, we presented an approach for capturing similarity between words that was concerned with the syntactic similarity of two strings. Today we are back to discuss another approach that is more concerned with the meaning of words. Semantic similarity is a confidence score that reflects the semantic relation between the meanings of two sentences. It is difficult to gain a high accuracy score because the exact semantic meanings are completely understood only in a particular context.
Matsig is an object-oriented signal class library for MATLAB 6.5 and later. It implements a signal class, simplifying operations and manipulations common in audio signal processing and speech processing
