The paper presents the results of assessment studies of the time course for technical wear in masonry buildings located in the area of mining-induced ground deformations. By using fuzzy inference system (FIS) and the “if-then” rule, corresponding language labels describing actual damage recorded in structure components were translated into scalar outputs describing the degree of damage to the building. Adopting this approach made it possible to separate damage resulting from additional effects coming from mining-induced ground deformations and the natural wear and tear of masonry structure. By using statistical analysis an exponential function for the condition of building damage and the function of natural wear and tear were developed. Both phenomena were subject to studies as a function of time regarding the technical age of building structure. The results obtained were used to develop a model for the course of technical wear of traditionally constructed buildings used within mining areas. In the course of natural wear and tear buildings located in mining areas are additionally exposed to forced ground deformations. The increase of internal forces in structure components induced by those effects results in creating an additional stress factor and damage. The hairline cracks and cracks of building structure components take place when the intensity value of mining effects becomes higher than the component stress resistance and repeated effects result in the decrease of structure rigidity. The observations of building behaviour in mining areas show that the intensity of mining activity and the multiplicity of its effect play a substantial role in the course of technical wear of buildings. The studies show that the level of damage resulting from mining effects adds up to natural wear and tear of the building and impairs the global technical condition as compared to similar buildings used outside mining areas.
The paper presents Gupta's relational decomposition technique expanded on linguistic level. It allows to reduce the hardware cost of the fuzzy system or the computing time of the final result, especially when referring to First Aggregation Then Inference (FATI) relational systems or First Inference Then Aggregation (FITA) rule systems. The inference result of the hierarchical system using decomposition technique is more fuzzy than of the classical system. The paper describes a linguistic decomposition technique based on partitioning the knowledge base of the fuzzy inference system. It allows to decrease or even totally remove a redundant fuzziness of the inference result.
For many adaptive noise control systems the Filtered-Reference LMS, known as the FXLMS algorithm is used to update parameters of the control filter. Appropriate adjustment of the step size is then important to guarantee convergence of the algorithm, obtain small excess mean square error, and react with required rate to variation of plant properties or noise nonstationarity. There are several recipes presented in the literature, theoretically derived or of heuristic origin. This paper focuses on a modification of the FXLMS algorithm, were convergence is guaranteed by changing sign of the algorithm steps size, instead of using a model of the secondary path. A TakagiSugeno-Kang fuzzy inference system is proposed to evaluate both the sign and the magnitude of the step size. Simulation experiments are presented to validate the algorithm and compare it to the classical FXLMS algorithm in terms of convergence and noise reduction.