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

Prediction of stroke probability occurrence based on fuzzy cognitive maps

Among neurological patients, stroke is the most common cause of mortality. It is a health problem that is very costly all over the world. Therefore, the mortality due to the disease can be reduced by identifying and modifying the risk factors. Controllable factors which are contributing to stroke including hypertension, diabetes, heart disease, hyperlipidemia, smoking, and obesity. Therefore, by identifying and controlling the risk factors, stroke can be prevented and the effects of this disease could be reduced to a minimum. Therefore, for the quick and timely diagnosis of the disease, we need an intelligent system to predict the stroke risk. In this paper, a method has been proposed for predicting the risk rate of stroke which is based on fuzzy cognitive maps and nonlinear Hebbian learning algorithm. The accuracy of the proposed NHL-FCM model is tested using 15-fold cross-validation, for 90 actual cases, and compared with those of support vector machine and k-nearest neighbours. The proposed method shows superior performance with a total accuracy of (95.4 ± 7.5)%

Exploring the Drivers of, and Potential Interventions to Reduce, Antimicrobial Resistance in the European Food System Context

Antimicrobial resistance (AMR) is a growing One Health problem that has become one of the leading causes of death worldwide. AMR emerges from a complex system characterized by multiple interacting factors across the human-animal-environment spectrum, all of which have the potential to be impacted by the effects of climate change. This thesis aimed to explore the drivers of AMR and assess potential interventions to reduce AMR in the Swedish food system context under potential climate change conditions. This thesis had four main objectives, to: 1) identify the quantitative and qualitative data needed to create and parameterize a simulation model of AMR emergence and transmission within the Swedish food system; 2) create and use a simulation model to test the potential ability of selected interventions to reduce AMR in the food system; 3) assess the sustainability of these interventions under climate change;, and 4) outline a systematic approach for creating mixed methods models for complex public health issues. The structure of the simulation model was based on an expert-derived causal loop diagram (CLD), created by Swedish and European AMR experts during a previously conducted participatory modelling workshop, that contained 91 nodes and 331 relationships deemed important to the development and spread of AMR within the Swedish food system. To determine if there was adequate information to create and parameterize the simulation model of AMR, a scoping review was conducted. This review identified 140 existing models and data from 414 sources to inform 64 of the major nodes within the CLD. The identified models addressed the main parts of the system (e.g., agriculture and farm transmission, antimicrobial use (AMU) and AMR, supply and demand for food); however, there was limited connection between the different areas of the food system. Nodes on the outer edges of the CLD did not have data, nor were they included within the scope of the models identified in the scoping review. Other data gaps included the environmental sector and wildlife. To further refine and parameterize the simulation model, semi-quantitative statements referring to the state of the nodes and relationships in the CLD were extracted from the transcripts from the prior participatory workshop. Transcript analysis identified 83 nodes, 48 of which were included in the CLD, and 35 were new nodes that emerged during the analysis or were existing nodes that were merged or divided. Based on the data requirements of the models identified via the scoping review, and the data currently available, it was not possible to create a fully quantitative model without including many assumptions. Therefore, the CLD was used as the base structure of a fuzzy cognitive map (FCM) of the Swedish food system, which was refined and parameterized by the data from the scoping review and transcript analysis. The final FCM contained 90 nodes, and 491 relationships. The use of FCM allowed for the evaluation of eight interventions under predicted climate change conditions, however, none of them were able to significantly reduce AMR in the system. Finally, the entire processes was reflected upon, including steps taken, challenges and mitigation strategies, and recommendations for future research in systems approaches for modelling complex systems and public health problems. In conclusion, this thesis identified that it was not feasible to create a purely quantitative model of AMR within the Swedish food system due to data limitations. However, by using data from the literature and experts’ tacit knowledge, an FCM of the system provided an innovative way to analyze the complex system, provided invaluable insight into the behaviour of the system, and aided in scenario analysis from a broader systems lens