Ahmadi S.,Monash University |
Forouzideh N.,Monash University |
Alizadeh S.,K. N. Toosi University of Technology |
Papageorgiou E.,Technological Education Institute TEI of Central Greece
Neural Computing and Applications | Year: 2015
In this paper, a new automated Fuzzy Cognitive Maps (FCMs) learning algorithm is developed to generate FCMs from historical data. Automated FCM learning algorithms are used to model and analyze systems which are very complex and cannot be handled by experts’ knowledge. The algorithm developed in this paper is based on the Imperialist Competitive Algorithm for global optimization and is called the Imperialist Competitive Learning Algorithm (ICLA). The ICLA divides the search space into several sections. It extracts the best knowledge from each section and follows a procedure to avoid local optima alongside rapid learning. Experiments have been conducted to compare the ICLA with other well-known FCM learning algorithms. The results show that in most cases, the ICLA performs better for learning FCMs in terms of solution accuracy and execution time. The testing results show clearly that the ICLA is a robust, fast and accurate FCM learning algorithm. © 2014, The Natural Computing Applications Forum.
Mourhir A.,Al Akhawayn University |
Rachidi T.,Al Akhawayn University |
Papageorgiou E.I.,Technological Education Institute TEI of Central Greece |
Karim M.,University Sidi Mohammed Ben Abdellah
Environmental Modelling and Software | Year: 2016
In this work we present the rational and design of a methodology to support Integrated Environmental Assessment using the DPSIR (Driving Forces-Pressures-State-Impact-Response) causal-effect framework and non-monotonic Fuzzy Cognitive Maps. The methodology is based on key pillars in environmental management, namely connecting the socioeconomic and the natural environment dimensions into a policy oriented context; integration of stakeholders with inter-sectorial synergies and tradeoffs; handling of ambiguities and uncertainties intrinsic to environmental modeling and representation of complex non-linear cause-effect relationships in the form of Fuzzy Inference Systems, capable of adapting dynamically the influence between indicators. The methodology has the potential to support the development of informed policies and improves reliability through transparent, traceable and reproducible results. The illustrative example assesses the impact of air pollution abatement policies according to expert perceptions using proactive scenarios; the results revealed that, despite some positive changes, air protection activities are missing an overall strategic vision. © 2015 Elsevier Ltd.