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.

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.

This study attempts to find a fuzzy logic system for assessing the quality of water in water treatment plants (WTPs) providing water for irrigation purposes in the Basrah Governorate (South of Iraq). Each month, samples are taken in each of six major WTPs to measure electrical conductivity ( EC), and the content of sodium, magnesium and calcium. The calculated value which is the sodium adsorption ratio ( SAR) is plotted with EC on the Richard diagram. SAR and EC values are combined together in a fuzzy inference system (FIS) to find out a quality number called the fuzzy irrigation water quality index number ( FIWQI) which ranges from zero to one. The higher the value of the index, the better water quality. The Richard diagram, which helps to classify irrigation water, is used to adjust FIS components. Results show that the FIWQI for all WTPs changes depending on location and season. It ranges between 0.114–0.170, 0.120–0.190, 0.114–0.170, 0.114–0.202, 0.118–0.500 and 0.46–0.500 for Al-Bradhaia 1, Al-Jubaila 1, Shatt Al-Arab, Garmmah 1, Al-Rebat, and Old Shauaibah WTPs, respectively. The results indicate that WTPs effluent drawn from the Shatt Al-Arab River has poor water quality for irrigation purposes, except for an Old Shauaibah which receives water from another source called a sweet water canal. FIS results are compared with values obtained from the Richard diagram and 96% degree of compatibility between the two methods is attained. This indicates that FIS is an acceptable method for water quality classification.

Hot deformation of metals is a widely used process to produce end products with the desired geometry and required mechanical properties. To properly design the hot forming process, it is necessary to examine how the tested material behaves during hot deformation. Model studies carried out to characterize the behaviour of materials in the hot deformation process can be roughly divided into physical and mathematical simulation techniques.
The methodology proposed in this study highlights the possibility of creating rheological models for selected materials using methods of artificial intelligence, such as neuro-fuzzy systems. The main goal of the study is to examine the selected method of artificial intelligence to know how far it is possible to use this method in the development of a predictive model describing the flow of metals in the process of hot deformation.
The test material was Inconel 718 alloy, which belongs to the family of austenitic nickel-based superalloys characterized by exceptionally high mechanical properties, physicochemical properties and creep resistance. This alloy is hardly deformable and requires proper understanding of the constitutive behaviour of the material under process conditions to directly enable the optimization of deformability and, indirectly, the development of effective shaping technologies that can guarantee obtaining products with the required microstructure and desired final mechanical properties.
To be able to predict the behaviour of the material under non-experimentally tested conditions, a rheological model was developed using the selected method of artificial intelligence, i.e. the Adaptive Neuro-Fuzzy Inference System (ANFIS).
The source data used in these studies comes from a material experiment involving compression of the tested alloy on a Gleeble 3800 thermo-mechanical simulator at temperatures of 900, 1000, 1050, 1100, 1150oC with the strain rates of 0.01 - 100 s-1 to a constant true strain value of 0.9.
To assess the ability of the developed model to describe the behaviour of the examined alloy during hot deformation, the values of yield stress determined by the developed model (ANFIS) were compared with the results obtained experimentally. The obtained results may also support the numerical modelling of stress-strain curves.

The prediction of rock cuttability to produce the lignite deposits in underground mining is important in excavation. Moreover, the certain geographic locations of rock masses for cuttability tests are also significant to apply and compare the rock cuttability parameters. In this study, sediment samples of two boreholes (Hole-1 and Hole-2) from the Sagdere Formation (Denizli Molasse Basin) were applied to find out the cerchar abrasivity index (CAI), rock quality designations (RQD), uniaxial compressive strengths, Brazilian tensile strengths and Shore hardnesses. The Sagdere Formation deposited in the terrestrial to shallow marine conditions consists mainly of conglomerates, sandstones, shales, lignites as well as reefal limestones coarse to fine grained. A dataset from the fine grained sediments (a part of the Sagdere Formation) have been created using rock parameters mentioned in the study. Dataset obtained were utilized to construct the best fitted statistical model for predicting CAI on the basis of multiple regression technique. Additionally, the relationships among the rock parameters were evaluated by fuzzy logic inference system whether the rock parameters used in the study can be correlated or not. When comparing the two statistical techniques, multiple regression method is more accurate and reliable than fuzzy logic inference method for the dataset in this study. Furthermore, CAI can be predicted by using UCS, BTS, SH and RQD values based on this study.

The continuous improvement in the industries and organizations hinges upon the evaluation of their performance. In fact, the performance evaluation assists organizations to identify their strengths and weaknesses and, accordingly, enhance their efficiency. As soon as the concept of sustainability was propounded in the engineering based industries, the performance evaluation got more importance due to the environmental issues and social concerns along with the economical aspects. Therefore, this paper is an attempt to propose an approach based on fuzzy best-worst method (BWM) and fuzzy inference system (FIS) in order to evaluate the performance of an Iranian steel complex in terms of sustainability concept. In the proposed approach, the weights of some selected criteria were determined by fuzzy BWM method and, then, the score of the under study industry was calculated in terms of economic, environmental, and social aspects. At the end, an FIS was developed to calculate the final score of the intended industry. In order to check the efficiency of the proposed approach, its performance was measured using expert knowledge as well as real data of a steel complex in Iran. A moderate to high performance has been achieved for the understudy case through conducting the proposed approach. It was suggested that the industry should focus on the criteria with both high weights and low evaluated scores (for example the environmental management technologies and knowledge criterion) to increase its performance evaluation score. The obtained results were indicative of the efficiency of the proposed approach.

Lightning is one of the causes of transmission disorders and natural phenomena that cannot be avoided. The South Sulawesi region is located close to the equator and has a high lightning density. This condition results in lightning susceptibility of disturbances to electrical system lines, especially in high-voltage airlines and substations. An Adaptive Neuro-Fuzzy Inference System (ANFIS) will show the Root Mean Square Error (RMSE) based on the membership function type. This journal is to predict the value of the transmission tower lightning density using the ANFIS method. The value of the lightning strike density index can later be determined based on ANFIS predictions. Analysis of the value calculation system of structural lightning strikes in the South Sulawesi region of the Sungguminasa-Tallasa route can be categorized as three characteristics lightning density (Nd). The calculation system results for the value of structural lightning struck in the South Sulawesi region and validated between manual calculations and ANFIS with an average percentage of 0.0554%.