Influence of climatic variables on wireless: case study Base-Station Receiver

The development of this research is done with the aim of finding the relationship betweenweather conditions and the loss of wireless connection. The data were obtained by ameteorological center of the area and a telecommunications company that operates in the sameplace. We studied different models based on fuzzy logic due to the easy interpretation the easyinterpretation of the rules and data management. We used the Weka application that providestools for pre-processing of data and Keel software tool for data classification. Nine classifiersbased on fuzzy rules were applied, where the Furia-C was that better results obtained in orderto quality and quantity of rules. In this scenario, a preprocessing of data were computed, wheresome techniques to improve the information was performed. Some of the obtained rulerscorroborate the influence of heavy rain over the loss of the signal, but other relationships thatincorporate new knowledge in the area, such as dew point and the average relative humidityappear

Influence of climatic variables on wireless: case study Base-Station Receiver

The development of this research is done with the aim of finding the relationship betweenweather conditions and the loss of wireless connection. The data were obtained by ameteorological center of the area and a telecommunications company that operates in the sameplace. We studied different models based on fuzzy logic due to the easy interpretation the easyinterpretation of the rules and data management. We used the Weka application that providestools for pre-processing of data and Keel software tool for data classification. Nine classifiersbased on fuzzy rules were applied, where the Furia-C was that better results obtained in orderto quality and quantity of rules. In this scenario, a preprocessing of data were computed, wheresome techniques to improve the information was performed. Some of the obtained rulerscorroborate the influence of heavy rain over the loss of the signal, but other relationships thatincorporate new knowledge in the area, such as dew point and the average relative humidityappear

A new approach to nonlinear modelling of dynamic systems based on fuzzy rules

For many practical weakly nonlinear systems we have their approximated linear model. Its parameters are known or can be determined by one of typical identification procedures. The model obtained using these methods well describes the main features of the system’s dynamics. However, usually it has a low accuracy, which can be a result of the omission of many secondary phenomena in its description. In this paper we propose a new approach to the modelling of weakly nonlinear dynamic systems. In this approach we assume that the model of the weakly nonlinear system is composed of two parts: a linear term and a separate nonlinear correction term. The elements of the correction term are described by fuzzy rules which are designed in such a way as to minimize the inaccuracy resulting from the use of an approximate linear model. This gives us very rich possibilities for exploring and interpreting the operation of the modelled system. An important advantage of the proposed approach is a set of new interpretability criteria of the knowledge represented by fuzzy rules. Taking them into account in the process of automatic model selection allows us to reach a compromise between the accuracy of modelling and the readability of fuzzy rules

A new approach to nonlinear modelling of dynamic systems based on fuzzy rules

For many practical weakly nonlinear systems we have their approximated linear model. Its parameters are known or can be determined by one of typical identification procedures. The model obtained using these methods well describes the main features of the system’s dynamics. However, usually it has a low accuracy, which can be a result of the omission of many secondary phenomena in its description. In this paper we propose a new approach to the modelling of weakly nonlinear dynamic systems. In this approach we assume that the model of the weakly nonlinear system is composed of two parts: a linear term and a separate nonlinear correction term. The elements of the correction term are described by fuzzy rules which are designed in such a way as to minimize the inaccuracy resulting from the use of an approximate linear model. This gives us very rich possibilities for exploring and interpreting the operation of the modelled system. An important advantage of the proposed approach is a set of new interpretability criteria of the knowledge represented by fuzzy rules. Taking them into account in the process of automatic model selection allows us to reach a compromise between the accuracy of modelling and the readability of fuzzy rules

A new approach to nonlinear modelling of dynamic systems based on fuzzy rules

For many practical weakly nonlinear systems we have their approximated linear model. Its parameters are known or can be determined by one of typical identification procedures. The model obtained using these methods well describes the main features of the system’s dynamics. However, usually it has a low accuracy, which can be a result of the omission of many secondary phenomena in its description. In this paper we propose a new approach to the modelling of weakly nonlinear dynamic systems. In this approach we assume that the model of the weakly nonlinear system is composed of two parts: a linear term and a separate nonlinear correction term. The elements of the correction term are described by fuzzy rules which are designed in such a way as to minimize the inaccuracy resulting from the use of an approximate linear model. This gives us very rich possibilities for exploring and interpreting the operation of the modelled system. An important advantage of the proposed approach is a set of new interpretability criteria of the knowledge represented by fuzzy rules. Taking them into account in the process of automatic model selection allows us to reach a compromise between the accuracy of modelling and the readability of fuzzy rules