Learning FCMs with multi-local and balanced memetic algorithms for forecasting industrial drying processes

In this paper, we propose a Fuzzy Cognitive Map (FCM) learning approach with a multi-local search in balanced memetic algorithms for forecasting industrial drying processes. The first contribution of this paper is to propose a FCM model by an Evolutionary Algorithm (EA), but the resulted FCM model is improved by a multi-local and balanced local search algorithm. Memetic algorithms can be tuned with different local search strategies (CMA-ES, SW, SSW and Simplex) and the balance of the effort between global and local search. To do this, we applied the proposed approach to the forecasting of moisture loss in industrial drying process. The thermal drying process is a relevant one used in many industrial processes such as food industry, biofuels production, detergents and dyes in powder production, pharmaceutical industry, reprography applications, textile industries, and others. This research also shows that exploration of the search space is more relevant than finding local optima in the FCM models tested

Situation Modeling of Regional Development in the Republic of Kazakhstan

The methodology of situation modeling was based on the application of fuzzy cognitive maps, indistinct regional data and indistinct time horizon. Based on indistinct number of involved concepts, the model enables users to create their own situations with fuzzy quantity of available concepts including both the existing and the added ones. The added concepts are characterized by the set properties and database related to no less than three fuzzy time horizons. The number of set impulses is fuzzy as well. Cognitive map training was based on the artificial intelligence element – the active Hebb learning rule. The impact of concepts was defined in the course of training. Fine adjustment of the fuzzy cognitive map was achieved by changing the training order using a rank scale and Saati’s sorting algorithm. The developed computer software was used in simulation modeling of regional socio-economic processes related to the project aiming at tourism development of the Alacol Lake in Almaty region. Research results are shown in the form of a fuzzy cognitive map reflecting internal and external relations within the region, graphs reflecting socio-economic development and the Bossel criterion. Simulation of allocations had a positive effect: GRP (Gross Regional Product) growth along with increase in employment and environmental improvement. The proposed approach provides a tool for forecasting of regional development and solution of different regional problems. This approach can be used with regard to any administrative-territorial entity, provided relevant statistical data

Situation Modeling of Regional Development in the Republic of Kazakhstan

The methodology of situation modeling was based on the application of fuzzy cognitive maps, indistinct regional data and indistinct time horizon. Based on indistinct number of involved concepts, the model enables users to create their own situations with fuzzy quantity of available concepts including both the existing and the added ones. The added concepts are characterized by the set properties and database related to no less than three fuzzy time horizons. The number of set impulses is fuzzy as well. Cognitive map training was based on the artificial intelligence element – the active Hebb learning rule. The impact of concepts was defined in the course of training. Fine adjustment of the fuzzy cognitive map was achieved by changing the training order using a rank scale and Saati’s sorting algorithm. The developed computer software was used in simulation modeling of regional socio-economic processes related to the project aiming at tourism development of the Alacol Lake in Almaty region. Research results are shown in the form of a fuzzy cognitive map reflecting internal and external relations within the region, graphs reflecting socio-economic development and the Bossel criterion. Simulation of allocations had a positive effect: GRP (Gross Regional Product) growth along with increase in employment and environmental improvement. The proposed approach provides a tool for forecasting of regional development and solution of different regional problems. This approach can be used with regard to any administrative-territorial entity, provided relevant statistical data

Super Fuzzy Matrices and Super Fuzzy Models for Social Scientists

This book introduces the concept of fuzzy super matrices and operations on them. This book will be highly useful to social scientists who wish to work with multi-expert models. Super fuzzy models using Fuzzy Cognitive Maps, Fuzzy Relational Maps, Bidirectional Associative Memories and Fuzzy Associative Memories are defined here. The authors introduce 13 multi-expert models using the notion of fuzzy supermatrices. These models are described with illustrative examples. This book has three chapters. In the first chaper, the basic concepts about super matrices and fuzzy super matrices are recalled. Chapter two introduces the notion of fuzzy super matrices adn their properties. The final chapter introduces many super fuzzy multi expert models.Comment: 280 page

Development of Adaptive Environmental Management System: A Participatory Approach through Fuzzy Cognitive Maps

Conference ProceedingsMining industries develop environmental management systems/plans to mitigate the impact their operations has on the society. Even with these plans, there are still issues of pollution affecting the society. Though there are ICT-based pollution monitoring solutions, their use is dismal due to lack of appreciation or understanding of the disseminated information. This result in mining communities depending on their own local knowledge to observe, monitor and predict miningrelated environmental pollution. However, this local knowledge has never been tested scientifically or analysed to recognize its usability or effectiveness. Mining companies tend to ignore this knowledge from the communities as it is treated like common information with no much scientific value. As a step towards verifying or validating this local knowledge, we demonstrate how fuzzy cognitive maps can be used to model, analyse and represent this linguistic local knowledge