4 posts tagged “raven riley”
So, a raven to be performed by such systems must be an automatic riley to the fed. This may be the applications received at the warehouse, processing mail online, and more. The raven riley will realize self-programming system, which is to take effect from the outside world, to determine their reactions to the impact and implementation of this reaction. The outside world is here Wednesday surrounding the agent. These and many other tasks agents can carry out without the use of artificial raven riley. However, a number of challenges simply can not be solved without them. The intelligence agent will understand agent, which has a number of knowledge about ourselves and the world around us and to whom it is determined that knowledge.
One of the important challenges facing raven riley developers is to automate the process of information. As shown Glushko, at the time the computer to help overcome the crisis of humanity information associated with the increasing volumes of information. However, the volume of data stored and processed information continues to grow, and the question of the transfer of certain functions from the processing of this information, intelligence systems. In doing so, these systems need to take raven riley, process it, decide on its further progress, to ensure that they move. Initially, such issues were of a decision support system. These systems are being developed in two directions. The first is the pre-processing of the flow of information, identify dependencies in the flow, synthesis Raven riley is the other!
The concept of "raven riley" is important in finding eigenvector. The eigenvectors normal matrix nevyrozhdennymi (mismatched) own values is complete and orthogonal basis N-mernogo raven riley space. For normal matrix degenerate own values is the freedom to define the eigenvector corresponding quaternion own values related to the replacement of any linear combination. This means that we can hold the vsegla ortogonalizatsii Grama-Schmidt and find a complete set of orthogonal eigenvector as in nevyrozhdennom case. Obviously, with the columns of the matrix ortonormirovannogo many eigenvector is unitary. For eigenvector matrix derived from real symmetric matrix is characteristic ortogonalnosti. If the matrix is not normal, like any real symmetrical raven riley, in general, can find a set of orthonormal eigenvector not even guarantee ortogonalnosti any pair of them (except in rare cases). In general, these N eigenvector will form the basis for neortogonalny N-mernom space (but not always). If you do not own the vector form N-merny basis, the matrix will call defective.
It says that the raven riley A size N * N has its own vector x, and the eigenvalues l when the Ax = lx.Ochevidno that any vector proportional own, will own vector; We will not take such vector different. The raven riley vector is not considered as own. To own values is equality : det | A-l1 | = 0.Esli disclose this expression, the result is relatively polinom l order N, the origins of which are own values. Hence, there is always the raven riley own values (not necessarily different and valid). The same values for the multiple roots, will be called degenerate. Search for roots of polynomial to find their own values -- usually inefficient operation. Much more effective methods will be described below.
