The article presents the use of the Mamdani fuzzy reasoning model to develop a proposal of a system controlling partnering relations in construction projects. The system input variables include: current assessments of particular partnering relation parameters, the weights of these parameters’ impact on time, cost, quality and safety of implementation of construction projects, as well as the importance of these project assessment criteria for its manager. For each of the partnering relation parameters, the project’s manager will receive controlrecommendations. Moreover, the parameter to be improved first will be indicated. The article contains a calculation example of the system’s operations.
The paper presents the operation of two neuro-fuzzy systems of an adaptive type, intended for solving problems of the approximation of multi-variable functions in the domain of real numbers. Neuro-fuzzy systems being a combination of the methodology of artiﬁcial neural networks and fuzzy sets operate on the basis of a set of fuzzy rules “if-then”, generated by means of the self-organization of data grouping and the estimation of relations between fuzzy experiment results. The article includes a description of neuro-fuzzy systems by Takaga-Sugeno-Kang (TSK) and Wang-Mendel (WM), and in order to complement the problem in question, a hierarchical structural self-organizing method of teaching a fuzzy network. A multi-layer structure of the systems is a structure analogous to the structure of “classic” neural networks. In its ﬁnal part the article presents selected areas of application of neuro-fuzzy systems in the ﬁeld of geodesy and surveying engineering. Numerical examples showing how the systems work concerned: the approximation of functions of several variables to be used as algorithms in the Geographic Information Systems (the approximation of a terrain model), the transformation of coordinates, and the prediction of a time series. The accuracy characteristics of the results obtained have been taken into consideration.
The quality of the squeeze castings is significantly affected by secondary dendrite arm spacing, which is influenced by squeeze cast input parameters. The relationships of secondary dendrite arm spacing with the input parameters, namely time delay, pressure duration, squeeze pressure, pouring and die temperatures are complex in nature. The present research work focuses on the development of input-output relationships using fuzzy logic approach. In fuzzy logic approach, squeeze cast process variables are expressed as a function of input parameters and secondary dendrite arm spacing is expressed as an output parameter. It is important to note that two fuzzy logic based approaches have been developed for the said problem. The first approach deals with the manually constructed mamdani based fuzzy system and the second approach deals with automatic evolution of the Takagi and Sugeno’s fuzzy system. It is important to note that the performance of the developed models is tested for both linear and non-linear type membership functions. In addition the developed models were compared with the ten test cases which are different from those of training data. The developed fuzzy systems eliminates the need of a number of trials in selection of most influential squeeze cast process parameters. This will reduce time and cost of trial experimentations. The results showed that, all the developed models can be effectively used for making prediction. Further, the present research work will help foundrymen to select parameters in squeeze casting to obtain the desired quality casting without much of time and resource consuming.
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.
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 basis for a mineral deposit delimitation is a qualitative and quantitative assessment of deposit parameters, qualifying a deposit as an economically valuable object. A conventional approach to the mineral deposit recognition and a deposit detailed parameters qualification in the initial stages of development work in the KGHM were presented in the paper. The goals of such recognition were defined, which through a gradual detailed expansion, resulting from the information inflow, allows for the construction of a more complete decision-making model. The description of the deposit parameters proposed in the article in the context of fuzzy logic, enables a presentation of imprecise statements and data, which may be a complement to a traditional description. Selected non-adjustable and adjustable s-norm and t-norm operators were demonstrated. Operators effects were tested for selected ore quality parameters (copper content and deposit thickness) by constructing adequate membership functions. In a practical application, the use of chosen fuzzy logic operators is proposed for the assessment of the qualitative parameters of copper-silver ore in the exploitation blocks for one of the mines belonging to KGHM Polish Copper S.A. The considerations have been extended by including the possibility of using compensation operators.
The operational mineral deposit reconnaissance tends to evaluate its parameters to conduct safe and profitable production. Particular deposit parameters, important from the point of mineral deposit management, are estimated on the basis of observations carried out by mining geological surveys. These observations usually involve sampling, drilling, laboratory analyses and others. The use of fuzzy description to assess the parameters of the mineral deposit was proposed in the paper. In the fuzzy characteristics, an imprecise descriptive description appeared in place of a particular numerical quantity. This approach was used to description of the ore deposit features (metal content, volume, and metal yield) by assigning them specific characteristic functions, whose distributions were based on basic statistical quantities. Characteristic functions can be used to prepare operational strategies for any configuration of required deposit parameters resulting from the production management needs. For this purpose, selected logical operators of fuzzy sets were used. In the next approach to fuzzy modeling, an opportunity to characterize the deposit in a subjective approach was indicated, where the assessment of the deposit parameters is based on rough, in some way, discretionary observation and evaluation. Such model construction enabled the overall assessment of the deposit from the point of view of any parameters. Through the implementation of appropriate inference rules, adequate fuzzy control planes were obtained, which may also be useful in the context of operational mine strategy planning.
Abstract. In this paper we present a new class of neuro-fuzzy systems designed for system modelling and pattern classi.cation. Our approach is characterized by automatic determination of fuzzy inference in the process of learning. Moreover, we introduce several .exibility concepts in the design of neuro-fuzzy systems. The method presented in the paper is characterized by high accuracy which outperforms previous techniques applied for system modelling and pattern classi.cation.
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.
A solar photovoltaic (PV) system has been emerging out as one of the greatest potential renewable energy sources and is contributing significantly in the energy sector. The PV system depends upon the solar irradiation and any changes in the incoming solar irradiation will affect badly on the output of the PV system. The solar irradiation is location specific and also the atmospheric conditions in the surroundings of the PV system contribute significantly to its performance. This paper presents the cumulative assessment of the four MPPT techniques during the partial shading conditions (PSCs) for different configurations of the PV array. The partial shading configurations like series-parallel, bridge link, total cross tied and honeycomb structure for an 8#2;4 PV array has been simulated to compare the maximum power point tracking (MPPT) techniques. The MPPT techniques like perturb and observe, incremental conductance, extremum seeking control and a fuzzy logic controller were implemented for different shading patterns. The results related to the maximum power tracked, tracking efficiency of each of the MPPT techniques were presented in order to assess the best MPPT technique and the best configuration of the PV array for yielding the maximum power during the PSCs.