Miklós F. Hatwágner

Advanced learning of fuzzy cognitive maps of waste management by bacterial algorithm

Fuzzy cognitive maps (FCMs) are a very convenient and simple tool for modeling complex systems. They are popular due to their simplicity and user friendliness. However, according to [1], human experts are subjective and can handle only relatively simple networks therefore there is an urgent need to develop methods for automated generation of FCM models. The present research deals with the methodology of FCMs in combination with the Bacterial Evolutionary Algorithm (BEA). The method of FCMs using BEA seems to be suitable to model such complex mechanisms as integrated municipal waste management (IMWM) systems. This paper is an attempt to assess the sustainability of the IMWM system by investigating the FCM methodology based on the BEA with a holistic approach. As a result, the best scenario to an IMWM system can be assigned.

A concept reduction approach for fuzzy cognitive map models in decision making and management

Abstract Policy making, strategic planning and management in general are complex decision making tasks, where the formulation of a quantitative mathematical model may be difficult or impossible due to lack of numerical data and dependence on imprecise verbal expressions. For such systems, knowledge representation graphs and cognitive maps are most familiar and often used for modelling complexity and aiding decision making. Fuzzy Cognitive Maps (FCM), as graph-based cognitive models, have been successfully used for knowledge representation and reasoning. In modelling complex systems usually a large number of concepts need to be considered. However, it is often difficult in real applications to find the appropriate number of concepts. Using only a few concepts is not enough to represent the modelled system with the required precision, and increasing the number of concepts increases the complexity of the model quadratically; it is burdensome to work with for the experts. The contribution of this paper is two-fold: (i) to propose a new concept reduction approach for FCM and (ii) to apply it on developing less complex FCM for management and decision making. The behaviour of reduced models is analysed through a number of scenarios with respect to the original complex system. The main idea of the reduction is a clustering based on fuzzy tolerance relations. The new approach is focused on reducing complexity in the modelling process, which provides a more transparent and easy to use model for policy makers. The applicability of the proposed method is demonstrated via literature examples and a solid waste management case study that initiated this research. The results clearly show the advantageous characteristics of the proposed concept reduction method for FCM and its aid in policy making.

Parameterization and concept optimization of FCM models

Fuzzy Cognitive Maps (FCM) are widely used to model and analyze the behavior of complex multicomponent systems. The application of FCM might be non-trivial in some specific context, however. Two rather general problems of the application of FCM and respective solutions are described in this paper. The first problem is as follows: In some cases the concept values obtained at the end of an FCM simulation are very similar. If this occurs, the order of concepts, thus their relative importance cannot well defined. The second problem is to select the appropriate concepts and to define their number. The concepts are given by human experts, but the selection of the appropriate concepts which help to reach the required accuracy of the model, while keeping the model as simple as possible is a difficult task. This paper deals with these two (connected) problems and proposes solutions for them. The proposed solution for the first problem is to choose the optimal λ parameter value in the threshold function, the one for the second is to apply a “state reduction method” based on fuzzy tolerance relations presented in the paper.

Expert-Based Method of Integrated Waste Management Systems for Developing Fuzzy Cognitive Map

Movement towards more sustainable waste management practice has been identified as a priority in the whole of EU. The EU Waste Management Strategy’s requirements emphasize waste prevention; recycling and reuse; and improving final disposal and monitoring. In addition, in Hungary the national waste strategy requires an increase in the household waste recycling and recovery rates. Integrated waste management system (IWMS) can be defined as the selection and application of suitable and available techniques, technologies and management programs to achieve waste management objectives and goals. In this paper, the concept of ‘key drivers’ are defined as factors that change the status quo of an existing waste management system in either positive or negative direction. Due to the complexity and uncertainty occurring in sustainable waste management systems, we propose the use of fuzzy cognitive map (FCM) and bacterial evolutionary algorithm (BEA) methods to support the planning and decision making process of integrated systems, as the combination of the FCM and BEA seem to be suitable to model complex mechanisms such as IWMS. Since the FCM is formed for a selected system by determining the concepts and their relationships, it is possible to quantitatively simulate the system considering its parameters. The goal of optimization was to find such a connection matrix for FCM that makes possible to generate the most similar time series. This way a more objective description of IWMS can be given. While the FCM model represents the IWMS as a whole, BEA is used for parameter optimization and identification. Based on the results, in the near future we intend to apply the systems of systems (SoS) approach to regional IWMS.

A New State Reduction Approach for Fuzzy Cognitive Map with Case Studies for Waste Management Systems

The authors have investigated the sustainability of Integrated Waste Management Systems (IWMS). These systems were modeled by Fuzzy Cognitive Maps (FCM), which are known as adequate fuzzy-neural network type models for multi-component systems with a stable state. The FCM model was designed of thirty-three factors to describe the real world processes of IWMS in as much detailed and as much accurately as possible. Although, this detailed model meets the requirements of accuracy, the presentation and explanation of such a complex model is difficult due to its size.

Strategic Decision Support in Waste Management Systems by State Reduction in FCM Models

In this paper, we introduce a new design for modeling sustainable waste management systems. By its complexity, this model is much more precise in describing the real systems than those found in the relevant literature. We set up a model with six factors and then decomposed the constituting factors up to around thirty subcomponents, thereby established an extremely complex and completely novel model of the Integrated Waste Management System (IWMS) using the system-of-system (SoS) approach with the help of experts. After the investigation of the basic and detailed model and their connection matrices, the following idea arises. The two models differ conceptually and so greatly that less than thirty-three factors should be enough to approximately describe the mechanism of action of the real IWMS. In the following, a new state reduction method is proposed. It can be considered as a generalization of the state reduction procedure of sequential systems and finite state machines. The essence of the proposal is to create clusters of factors and to build a new model using these clusters as factors. This way the number of factors can be decreased to make the model easier to understand and use. Our main goal with this method is to support the strategic decision making process of the stakeholder in order to ensure the long-term sustainability of IWMS.

Parameter dependence of fuzzy cognitive maps' behaviour

Stakeholder Relationship Management Systems (SRMS) are conventionally analyzed by a static way, which hides the interconnections of the system. Authors investigated a novel approach to make the evaluation of the interconnections of the SRMS and their behavior achievable. The Fuzzy Cognitive Maps (FCM) is a proper tool to investigate the properties of SRMS. The simulation of SRMS with FCM supports the business management process and different project support activities. If the factors of SRMS themselves, the initial states of them and causality relations among them are already determined, a simulation can be easily carried out. In some specific situations the results of the simulation is hard to use in practice, however. If the differences between factor states are very small, the order (importance) of factors cannot be defined. In such cases the modification of the threshold functions parameter can help to better separate final factor states. This paper deals with the investigation of this approach.

Modeling integrated sustainable waste management systems by fuzzy cognitive maps and the system of systems concept

Modeling integrated sustainable waste management systems by fuzzy cognitive maps and the system of systems concept

Searching for a nonlinear ODE model of vehicle crash with genetic optimization

Vehicle crash is a very complex process, which can be modelled in details using the finite element method (FEM), but a simple, quasi-heuristic model with a limited number of parameters is often more beneficial. In this paper we propose a relatively simple dynamic model for deformation and force during a frontal collision process, which has very similar behavior to the experimental data. A genetic-type optimization of model parameters is executed on three car crash experimental data sets.

Banking Applications of FCM Models

Fuzzy Cognitive Map (FCMs) is an appropriate tool to describe, qualitatively analyze or simulate the behavior of complex systems. FCMs are bipolar fuzzy graphs: their building blocks are the concepts and the arcs. Concepts represent the most important components of the system, the weighted arcs define the strength and direction of cause-effect relationships among them. FCMs are created by experts in several cases. Despite the best intention the models may contain subjective information even if it was created by multiple experts. An inaccurate model may lead to misleading results, therefore it should be further analyzed before usage. Our method is able to automatically modify the connection weights and to test the effect of these changes. This way the hidden behavior of the model and the most influencing concepts can be mapped. Using the results the experts may modify the original model in order to achieve their goal. In this paper the internal operation of a department of a bank is modeled by FCM. The authors show how the modification of the connection weights affect the operation of the institute. This way it is easier to understand the working of the bank, and the most threatening dangers of the system getting into an unstable (chaotic or cyclic state) can be identified and timely preparations become possible.