Modelling business and management systems using Fuzzy cognitive maps: A critical overview

A critical overview of modelling Business and Management (B&M) Systems using Fuzzy Cognitive Maps is presented. A limited but illustrative number of specific applications of Fuzzy Cognitive Maps in diverse B&M systems, such as e business, performance assessment, decision making, human resources management, planning and investment decision making processes is provided and briefly analyzed. The limited survey is given in a table with statics of using FCMs in B&M systems during the last 15 years. The limited survey shows that the applications of Fuzzy Cognitive Maps to today’s Business and Management studies has been steadily increased especially during the last 5-6 years. Interesting conclusions and future research directions are highlighted

Why Modeling Complex Dynamic Systems using Fuzzy Cognitive Maps?

The difficult problem of modeling Complex Dynamic Systems (CDS) is carefully reviewed. Main characteristics of CDS are considered and analyzed. Today’s mathematical models and approaches cannot provide satisfactory answers to the challenging problems of the society. The key problem of complex dynamic systems and control theory consists in the development of methods of qualitative analysis of the dynamics and behavior of such systems and in the construction of efficient control algorithms for their efficient operation. The purpose of control to bring the system to a point of its phase space which corresponds to maximal or minimal value of the chosen efficiency criterion is reviewed and analyzed. The reasons for using Fuzzy Cognitive Maps (FCMs) in modeling Complex dynamic Systems are provided. The basics of FCMs are briefly presented. An illustrative example is considered and interesting results are presented and discussed

Why Modeling Complex Dynamic Systems using Fuzzy Cognitive Maps?

The difficult problem of modeling Complex Dynamic Systems (CDS) is carefully reviewed. Main characteristics of CDS are considered and analyzed. Today’s mathematical models and approaches cannot provide satisfactory answers to the challenging problems of the society. The key problem of complex dynamic systems and control theory consists in the development of methods of qualitative analysis of the dynamics and behavior of such systems and in the construction of efficient control algorithms for their efficient operation. The purpose of control to bring the system to a point of its phase space which corresponds to maximal or minimal value of the chosen efficiency criterion is reviewed and analyzed. The reasons for using Fuzzy Cognitive Maps (FCMs) in modeling Complex dynamic Systems are provided. The basics of FCMs are briefly presented. An illustrative example is considered and interesting results are presented and discusse

Control aspects of the Schuchuli Village stand-alone photovoltaic power system

A photovoltaic power system in an Arizona Indian village was installed. The control subsystem of this photovoltaic power system was analyzed. The four major functions of the control subsystem are: (1) voltage regulation; (2) load management; (3) water pump control; and (4) system protection. The control subsystem functions flowcharts for the control subsystem operation, and a computer program that models the control subsystem are presented

Modelling business and management systems using Fuzzy cognitive maps: A critical overview

A critical overview of modelling Business and Management (B&M) Systems using Fuzzy Cognitive Maps is presented. A limited but illustrative number of specific applications of Fuzzy Cognitive Maps in diverse B&M systems, such as e business, performance assessment, decision making, human resources management, planning and investment decision making processes is provided and briefly analyzed. The limited survey is given in a table with statics of using FCMs in B&M systems during the last 15 years. The limited survey shows that the applications of Fuzzy Cognitive Maps to today’s Business and Management studies has been steadily increased especially during the last 5-6 years. Interesting conclusions and future research directions are highlighted

A Critical Overview of Data Mining for Business Applications

Everybody looks to a world that does not remain the same. Furthermore no one can deny that the world is changing, and changing very fast. Technology, education, science, environment, health, communicating habits, entertainment, eating habits, dress - there is hardly anything in life that is not changing. Some changes we like, while others create fear and anxiety around us. Everywhere there is a feeling of insecurity. What will happen to us tomorrow, or what will happen to our children, are questions we keep frequently asking. One thing, however, is clear. It is no more possible to live in the way we have been living so far. It seems that now the entire fabric of life will have to be changed. Life will have to be redesigned. The life of the individual, the social structure, the working conditions and governance all will have to be re-planned. Furthermore over the past 2-3 decades there has been a huge increase in the amount of data being stored in databases as well as the number of database applications in business and the scientific domain. This explosion in the amount of electronically stored data was accelerated by the success of the relational model for storing data and the development and maturing of data retrieval and manipulation technologies. While technology for storing the data developed fast to keep up with the demand, little stress was paid to developing software for analysing the data until recently when companies realized that hidden within these masses of data was a resource that was being ignored. The huge amounts of stored data contains knowledge on a good number of aspects of their business waiting to be harnessed and used for more effective business decision support. Database Management Systems (DMS) used to manage these data sets at present only allow the user to access information explicitly present in the databases i.e. the data. The data stored in the database is only a small part of the \u27iceberg of information\u27 available from it. Contained implicitly within this data is knowledge about a number of aspects of their business waiting to be harnessed and used for more effective business decision support. This extraction of knowledge from large data sets is called Data Mining or Knowledge Discovery in Databases and is defined as the non-trivial extraction of implicit, previously unknown and potentially useful information from data. Almost in parallel with the developments in the database field, machine learning research was maturing with the development of a number of sophisticated techniques based on different models of human learning. Learning by example, cased-based reasoning, learning by observation and neural networks are some of the most popular learning techniques that were being used to create the ultimate thinking machine

The subjective effect of multiple co-channel frequency modulated television interference

As the geostationary orbit/spectrum becomes saturated, there is a need for the ability to reuse frequency assignments. Protection ratios (the ratio of wanted signal power to interfering signal power at the receiver) play a key role in determining efficient frequency reuse plans. A knowledge of the manner in which multiple sources of co-channel interference combine is vital in determining protection ratio requirements such that suitable margin may be allocated for multiple interfering signals. Results of tests examining the subjective assessment of multiple co-channel frequency modulated television signals interfering with another frequency modulated TV system are presented

An Overview of Zero Energy Buildings with an Emphasis on Energy Savings

The indiscriminate exploitation of the planet\u27s energy sources has nowadays led to an uncontrolled form of climate change, with negative consequences for both humankind and the environment. In an effort to curb this situation, energy savings in both the building sector and other sectors of activity (industry, transport, etc.) is a primary objective and an indispensable component of any energy policy been planned today all around the world. As the building sector consumes 40% of the energy required at European level, the European Union (EU) has made considerable efforts to significantly reduce these consumption levels. In recent years, the European Union’s efforts have been made to improve the situation in energy policy and planning issues, focusing on energy saving as well as the use of renewable energy sources that have proven to have significant benefits for the environment, society and the economy. The European Council adopted a package and the European Parliament voted it and the 27 Heads of State and governments finally agreed to implement the 20-20-20 EU Energy targets: by 2020, reduce by 20% the emissions of greenhouse gases, increase by 20% the energy efficiency in the EU and to reach 20% of renewable energy sources (RES) in total energy consumption in the EU.It is characteristic that in the context of saving efforts in the building sector, provisions have been incorporated into the requirements of net Zero Energy Buildings (nZEB)both in the corresponding Community Directives. These provisions provide for the construction of all new buildings by 2021 at the latest with net zero consumption standards. In particular for public new buildings, the time horizon is even shorter. These targets difficult will be met.In this paper a review of definitions and methodologies of nZEB will be presented. It also examines the possibility of integrating two different systems of solar energy utilization into a residential building in the Patras area in order to achieve its energy self-sufficiency and hence its classification in the category of Zero Energy Consumption Buildings. More specifically, the integration of both photovoltaic and hybrid photovoltaic - thermal collectors was examined. The mathematical model for this proposed hybrid energy system is presented and analyzed. Examining these systems against other RES technologies was preferred because of their easier integration in building infrastructure but also the greater familiarity that users of the buildings usually have with both photovoltaic and solar thermal systems. The modeling of the operation of the two systems was done using the Matlab programming environment, taking into account the relevant literature and data available from manufacturers of related equipment. Different formats were developed to model the two technologies due to the different mathematical equations governing the operation of each of the proposed systems. Modeling took place for four representative months of the year (January, April, July and October) in the Western part of Greece. Obtained simulation results which have been very promising will be presented at the conference

State Feedback of Complex Systems using Fuzzy Cognitive Maps

Complex systems have become a research area with increasing interest over the last years. The emergence of new technologies, the increase in computational power with reduced resources and cost, the integration of the physical world with computer based systems has created the possibility of significantly improving the quality of life of humans. While a significant degree of automation within these systems exists and has been provided in the past decade with examples of the smart homes and energy efficient buildings, a paradigm shift towards autonomy has been noted. The need for autonomy requires the extraction of a model; while a strict mathematical formulation usually exists for the individual subsystems, finding a complete mathematical formulation for the complex systems is a near impossible task to accomplish. For this reason, methods such as the Fuzzy Cognitive Maps (FCM) have emerged that are able to provide with a description of the complex system. The system description results from empirical observations made from experts in the related subject – integration of expert’s knowledge – that provide the required cause-effect relations between the interacting components that the FCM needs in order to be formulated. Learning methods are employed that are able to improve the formulated model based on measurements from the actual system. The FCM method, that is able to inherently integrate uncertainties, is able to provide an adequate model for the study of a complex system. With the required system model, the next step towards the development of a autonomous systems is the creation of a control scheme. While FCM can provide with a system model, the system representation proves inadequate to be utilized to design classic model based controllers that require a state space or frequency domain representation. In state space representation, the state vector contains the variables of the system that can describe enough about the system to determine its future behavior in absence of external variables. Thus, within the components – the nodes of the FCM, ideally those can be identified that constitute the state vector of the system. In this work the authors propose the creation of a state feedback control law of complex systems via Fuzzy Cognitive Maps. Given the FCM representation of a system, initially the components-states of the system are identified. Given the identified states, a FCM representation of the controller occurs where the controller parameters are the weights of the cause-effect relations of the system. The FCM of the system then is augmented with the FCM of the controller. An example of the proposed methodology is given via the use of the cart-pendulum system, a common benchmark system for testing the efficiency of control systems

State Feedback of Complex Systems Using Fuzzy Cognitive Maps

Complex systems have become a research area with increasing interest over the last years. The emergence of new technologies, the increase in computational power with reduced resources and cost, the integration of the physical world with computer based systems has created the possibility of significantly improving the quality of life of humans. While a significant degree of automation within these systems exists and has been provided in the past decade with examples of the smart homes and energy efficient buildings, a paradigm shift towards autonomy has been noted. The need for autonomy requires the extraction of a model; while a strict mathematical formulation usually exists for the individual subsystems, finding a complete mathematical formulation for the complex systems is a near impossible task to accomplish. For this reason, methods such as the Fuzzy Cognitive Maps (FCM) have emerged that are able to provide with a description of the complex system. The system description results from empirical observations made from experts in the related subject – integration of expert’s knowledge – that provide the required cause-effect relations between the interacting components that the FCM needs in order to be formulated. Learning methods are employed that are able to improve the formulated model based on measurements from the actual system. The FCM method, that is able to inherently integrate uncertainties, is able to provide an adequate model for the study of a complex system. With the required system model, the next step towards the development of a autonomous systems is the creation of a control scheme. While FCM can provide with a system model, the system representation proves inadequate to be utilized to design classic model based controllers that require a state space or frequency domain representation. In state space representation, the state vector contains the variables of the system that can describe enough about the system to determine its future behavior in absence of external variables. Thus, within the components – the nodes of the FCM, ideally those can be identified that constitute the state vector of the system. In this work the authors propose the creation of a state feedback control law of complex systems via Fuzzy Cognitive Maps. Given the FCM representation of a system, initially the components-states of the system are identified. Given the identified states, a FCM representation of the controller occurs where the controller parameters are the weights of the cause-effect relations of the system. The FCM of the system then is augmented with the FCM of the controller. An example of the proposed methodology is given via the use of the cart-pendulum system, a common benchmark system for testing the efficiency of control systems