|
|
| (97 intermediate revisions by the same user not shown) |
| Line 1: |
Line 1: |
| <pre>
| |
| In 1991 the idea that the apparatus of Generalized Nets (GNs, see [A1, A2,
| |
| A3, A4]) can be used as a mathematical foundation of the Artificial
| |
| Intelligence (AI) was introduced (see [A5]). During the last years, a lot of
| |
| results were obtained and a lot of papers and books were published connected
| |
| to the realization of this idea. Already, there are GN-models describing the
| |
| way of functioning and the results of the work of separate types of
| |
| databases and expert systems. The separate types of genetic algorithms and
| |
| of ant collony optimizations are described by GNs. The processes of pattern
| |
| and speech recognition and of scene analysis also are represented by GNs.
| |
|
| |
|
|
| |
|
| |
| Now it is clear that the GNs are not only a tool for modelling of processes,
| |
| but a methodology for their description, simulation and (that is the most
| |
| import) extension. After describing of the expert system by a GN, there was
| |
| shown that the concept of an expert system can be extended with new
| |
| components, so that from one side the new expert system also to be modelled
| |
| by a GN, and from another hand, the new expert system to have essentially
| |
| new components. For example, expert systems with priorities of their facts
| |
| and roles were introduced. Expert systems with special meta-facts, that
| |
| change the rules, but that are essentially more suitable for applying were
| |
| constructed. As extensions of fuzzy expert systems, intuitionistic fuzzy
| |
| expert systems were defined. Expert systems that can answer to temporal and
| |
| modal questions were described.
| |
|
| |
|
| |
|
| |
| In the present book the authors collect a part of their research in the area
| |
| of Neural Networks (NNs). Here we construct GNs representing the way of
| |
| functioning and the results of the work of separate types of NNs. Here we
| |
| describe feedforward types of NNs as multilayer perceptron (MPL) by GNs. Here, we
| |
| give also GNs that determine the optimal forms of NNs and MPL using golden
| |
| sections algorithm. The possibility of the parallel work and transfer of information
| |
| among a lot of NNs, but in the frames of one GN is discussed.
| |
|
| |
|
| |
|
| |
| In the second book we will discuss the program realization of the GN-models,
| |
| describing here.
| |
|
| |
| So, we like to show that the apparatus of the GNs is a suitable tool for
| |
| modelling of NNs.
| |
|
| |
| </pre>
| |