The issue of line simplification is one of the fundamental problems of generalisation of geographical information, and the proper parameterisation of simplification algorithms is essential for the correctness and cartographic quality of the results. The authors of this study have attempted to apply computational intelligence methods in order to create a cartographic knowledge base that would allow for non-standard parameterisation of WEA (Weighted Effective Area) simplification algorithm. The aim of the conducted research was to obtain two independent methods of non-linear weighting of multi-dimensional regression function that determines the “importance” of specific points on the line and their comparison to each other. The first proposed approach consisted in the preparation of a set of cartographically correct examples constituting a basis for teaching a neural network, while the other one consisted in defining inference rules using fuzzy logic. The obtained results demonstrate that both methods have great potential, although the proposed solutions require detailed parameterisation taking into account the specificity of geometric variety of the source data.
One way to ensure the required technical characteristics of castings is the strict control of production parameters affecting the quality of
the finished products. If the production process is improperly configured, the resulting defects in castings lead to huge losses. Therefore,
from the point of view of economics, it is advisable to use the methods of computational intelligence in the field of quality assurance and
adjustment of parameters of future production. At the same time, the development of knowledge in the field of metallurgy, aimed to raise
the technical level and efficiency of the manufacture of foundry products, should be followed by the development of information systems
to support production processes in order to improve their effectiveness and compliance with the increasingly more stringent requirements
of ergonomics, occupational safety, environmental protection and quality. This article is a presentation of artificial intelligence methods
used in practical applications related to quality assurance. The problem of control of the production process involves the use of tools such
as the induction of decision trees, fuzzy logic, rough set theory, artificial neural networks or case-based reasoning.