Applying a Revised Approach of Fuzzy Cognitive Maps on a Hybrid Electrical Energy System

Complex systems modeling is a rapidly developing research field which incorporates various scientific sectors from bio medicine and energy to economic and social sciences. However, as the systems’ complexity increases pure mathematical modeling techniques prove to be a rather laborious task which demands wasting many resources and in many occasions, could not lead to the desired system response. This realization led researchers turn their attention into the field of computational intelligence; Neural Networks and Fuzzy Logic etc. In this way scientists were able to provide a model of a system which is strongly characterized by fuzziness and uncertainties. Fuzzy Cognitive Maps (FCM) in another methodology which lies in the field of computational intelligence. FCM came as a combination of Neural Networks and Fuzzy Logic and were first introduced by B. Kosko in 1986. All these years they have been applied on a variety of systems such as social, psychological, medical, agricultural, marketing, business management, energy, advertising etc, both for systems modeling and decision-making support systems, with very promising results. Classical FCM approach uses the experts’ knowledge in order to create the initial knowledge base of each system. Based on the experts’ knowledge, the interrelations among the system variables are determined and the system response is defined. Through years, improvements have been made and learning algorithms were embodied in the initial approach. Learning algorithms used data information and history to update the weights (the interconnections) among concepts (variables), contributed to the optimization of FCMs and reached more efficient systems’ response. However, all these decades, researchers have mentioned some weak points as well. In the last years substantial research has been made in order to overcome some of the well-known limitations of the FCM methodology. This paper will apply a revised approach of the Fuzzy Cognitive Maps method on a techno-economic study of an autonomous hybrid system photovoltaic and geothermal energy Specifically, the FCM model of this system includes twenty-five concepts and three of them are considered as outputs, the total system efficiency, the total energy production and the total system cost. The aim of the study is to provide maximum performance with the minimum total cost. To this end results for both the classic and revised approach of the FCM method are provided and discussed. Computational Intelligence and especially Fuzzy Cognitive Maps are a very promising field in modeling complex systems. The latest approaches of the method show that FCM can open new paths towards higher efficiency, more accurate models and effective decision-making results

Applying a Revised Approach of Fuzzy Cognitive Maps on a Hybrid Electrical Energy System

Complex systems modeling is a rapidly developing research field which incorporates various scientific sectors from bio medicine and energy to economic and social sciences. However, as the systems’ complexity increases pure mathematical modeling techniques prove to be a rather laborious task which demands wasting many resources and in many occasions, could not lead to the desired system response. This realization led researchers turn their attention into the field of computational intelligence; Neural Networks and Fuzzy Logic etc. In this way scientists were able to provide a model of a system which is strongly characterized by fuzziness and uncertainties. Fuzzy Cognitive Maps (FCM) in another methodology which lies in the field of computational intelligence. FCM came as a combination of Neural Networks and Fuzzy Logic and were first introduced by B. Kosko in 1986. All these years they have been applied on a variety of systems such as social, psychological, medical, agricultural, marketing, business management, energy, advertising etc, both for systems modeling and decision-making support systems, with very promising results. Classical FCM approach uses the experts’ knowledge in order to create the initial knowledge base of each system. Based on the experts’ knowledge, the interrelations among the system variables are determined and the system response is defined. Through years, improvements have been made and learning algorithms were embodied in the initial approach. Learning algorithms used data information and history to update the weights (the interconnections) among concepts (variables), contributed to the optimization of FCMs and reached more efficient systems’ response. However, all these decades, researchers have mentioned some weak points as well. In the last years substantial research has been made in order to overcome some of the well-known limitations of the FCM methodology. This paper will apply a revised approach of the Fuzzy Cognitive Maps method on a techno-economic study of an autonomous hybrid system photovoltaic and geothermal energy Specifically, the FCM model of this system includes twenty-five concepts and three of them are considered as outputs, the total system efficiency, the total energy production and the total system cost. The aim of the study is to provide maximum performance with the minimum total cost. To this end results for both the classic and revised approach of the FCM method are provided and discussed. Computational Intelligence and especially Fuzzy Cognitive Maps are a very promising field in modeling complex systems. The latest approaches of the method show that FCM can open new paths towards higher efficiency, more accurate models and effective decision-making results

ZASTOSOWANIE ROZMYTEJ MAPY KOGNITYWNEJ W PROGNOZOWANIU EFEKTYWNOŚCI PRACY WYPOŻYCZALNI ROWEROWYCH

This paper proposes application of fuzzy cognitive map with evolutionary learning algorithms to model a system for prediction of effectiveness of bike sharing systems. Fuzzy cognitive map was constructed based on historical data and next used to forecast the number of cyclists and customers of bike sharing systems on three consecutive days. The learning process was realized with the use of Individually Directional Evolutionary Algorithm IDEA and Real-Coded Genetic Algorithm RCGA. Simulation analysis of the system for prediction of effectiveness of bike sharing systems was carried out with the use of software developed in JAVA.W pracy zaproponowano zastosowanie rozmytej mapy kognitywnej wraz z ewolucyjnymi algorytmami uczenia do modelowania systemu prognozowania efektywności pracy wypożyczalni rowerowych. Na podstawie danych historycznych zbudowano rozmytą mapę kognitywną, którą następnie zastosowano do prognozowania liczby rowerzystów i klientów wypożyczalni w trzech kolejnych dniach. Proces uczenia zrealizowano z zastosowaniem indywidualnego kierunkowego algorytmu ewolucyjnego IDEA oraz algorytmu genetycznego z kodowaniem zmiennoprzecinkowym RCGA. Analizę symulacyjną systemu prognozowania efektywności pracy wypożyczalni rowerowych przeprowadzono przy pomocy oprogramowania opracowanego w technologii JAVA

ZMODYFIKOWANA METODA UŻYCIA FUNKCJI PROGOWYCH W MODELACH RELACYJNYCH ROZMYTYCH MAP KOGNITYWNYCH

The article describes the method for introducing a threshold function into a model of the Relational Fuzzy Cognitive Map that uses fuzzy numbers arithmetic. Processing the fuzzy number by function causes deformations of the shape if this number and its support. It is proposed a geometrical approach to this problem, allowing to maintain constancy of the support and to keep the shape of the processed fuzzy number with an appropriate shift of the center of this number. The method was tested on fuzzy numbers with different membership functions.W artykule przedstawiono sposób wprowadzenia funkcji progowej do modelu Relacyjnej Rozmytej Mapy Kognitywnej, wykorzystującego arytmetykę liczb rozmytych. Funkcyjne przetwarzanie liczby rozmytej powoduje deformacje kształtu liczby i jej nośnika. Zaproponowano „geometryczne” podejście do tego problemu, pozwalające zachować niezmienność nośnika oraz utrzymać kształt przetwarzanej liczby rozmytej przy jednoczesnym odpowiednim przesunięciu centrum tej liczby. Metoda została przetestowana na liczbach rozmytych o różnych funkcjach przynależności

FCMpy: A Python Module for Constructing and Analyzing Fuzzy Cognitive Maps

FCMpy is an open source package in Python for building and analyzing Fuzzy Cognitive Maps. More specifically, the package allows 1) deriving fuzzy causal weights from qualitative data, 2) simulating the system behavior, 3) applying machine learning algorithms (e.g., Nonlinear Hebbian Learning, Active Hebbian Learning, Genetic Algorithms and Deterministic Learning) to adjust the FCM causal weight matrix and to solve classification problems, and 4) implementing scenario analysis by simulating hypothetical interventions (i.e., analyzing what-if scenarios).Comment: 22 pages, 9 Figure