An Evolutionary-Based Similarity Reasoning Scheme for Monotonic Multi-Input Fuzzy Inference Systems

In this paper, an Evolutionary-based Similarity Reasoning (ESR) scheme for preserving the monotonicity property of the multi-input Fuzzy Inference System (FIS) is proposed. Similarity reasoning (SR) is a useful solution for undertaking the incomplete rule base problem in FIS modeling. However, SR may not be a direct solution to designing monotonic multi-input FIS models, owing to the difficulty in getting a set of monotonically-ordered conclusions. The proposed ESR scheme, which is a synthesis of evolutionary computing, sufficient conditions, and SR, provides a useful solution to modeling and preserving the monotonicity property of multi-input FIS models. A case study on Failure Mode and Effect Analysis (FMEA) is used to demonstrate the effectiveness of the proposed ESR scheme in undertaking real world problems that require the monotonicity property of FIS models

Sustainability Performance Assessment Using Self-Organizing Maps (SOM) and Classification and Ensembles of Regression Trees (CART)

This study aims to develop a new approach based on machine learning techniques to assess sustainability performance. Two main dimensions of sustainability, ecological sustainability, and human sustainability, were considered in this study. A set of sustainability indicators was used, and the research method in this study was developed using cluster analysis and prediction learning techniques. A Self-Organizing Map (SOM) was applied for data clustering, while Classification and Regression Trees (CART) were applied to assess sustainability performance. The proposed method was evaluated through Sustainability Assessment by Fuzzy Evaluation (SAFE) dataset, which comprises various indicators of sustainability performance in 128 countries. Eight clusters from the data were found through the SOM clustering technique. A prediction model was found in each cluster through the CART technique. In addition, an ensemble of CART was constructed in each cluster of SOM to increase the prediction accuracy of CART. All prediction models were assessed through the adjusted coefficient of determination approach. The results demonstrated that the prediction accuracy values were high in all CART models. The results indicated that the method developed by ensembles of CART and clustering provide higher prediction accuracy than individual CART models. The main advantage of integrating the proposed method is its ability to automate decision rules from big data for prediction models. The method proposed in this study could be implemented as an effective tool for sustainability performance assessment

Sustainability performance assessment using self-organizing maps (SOM) and classification and ensembles of regression trees (CART)

This study aims to develop a new approach based on machine learning techniques to assess sustainability performance. Two main dimensions of sustainability, ecological sustainability, and human sustainability, were considered in this study. A set of sustainability indicators was used, and the research method in this study was developed using cluster analysis and prediction learning techniques. A Self-Organizing Map (SOM) was applied for data clustering, while Classification and Regression Trees (CART) were applied to assess sustainability performance. The proposed method was evaluated through Sustainability Assessment by Fuzzy Evaluation (SAFE) dataset, which comprises various indicators of sustainability performance in 128 countries. Eight clusters from the data were found through the SOM clustering technique. A prediction model was found in each cluster through the CART technique. In addition, an ensemble of CART was constructed in each cluster of SOM to increase the prediction accuracy of CART. All prediction models were assessed through the adjusted coefficient of determination approach. The results demonstrated that the prediction accuracy values were high in all CART models. The results indicated that the method developed by ensembles of CART and clustering provide higher prediction accuracy than individual CART models. The main advantage of integrating the proposed method is its ability to automate decision rules from big data for prediction models. The method proposed in this study could be implemented as an effective tool for sustainability performance assessment

Un enfoque de sustentabilidad utilizando lógica difusa y minería de datos

[ES] Sustainable development goals are now the agreed criteria to monitor states, and this work will demonstrate that numerical and graphical methods are valuable tools in assessing progress. Fuzzy Logic is a reliable procedure for transforming human qualitative knowledge into quantitative variables that can be used in the reasoning of the type “if, then” to obtain answers pertaining to sustainability assessment. Applications of machine learning techniques and artificial intelligence procedures span almost all fields of science. Here, for the first-time, unsupervised machine learning is applied to sustainability assessment, combining numerical approaches with graphical procedures to analyze global sustainability. CD HJ-Biplots to portray graphically the sustainability position of a large number of countries are a useful complement to mathematical models of sustainability. Graphical information could be useful to planners it shows directly how countries are grouped according to the most related sustainability indicators. Thus, planners can prioritize social, environmental, and economic policies and make the most effective decisions. One could graphically observe the dynamic evolution of sustainability worldwide over time with a graphical approach used to draw relevant conclusions. In an era of climate change, species extinction, poverty, and environmental migration, such observations could aid political decision-making regarding the future of our planet. A large number of countries remain in the areas of moderate or low sustainability. Fuzzy logic has proven to be an uncontested numerical method as it occurs with SAFE. An unsupervised learning method called Variational Autoencoder interplay Graphical Analysis (VEA&GA) has been proposed, to support sustainability performance with appropriate training data. The promising results show that this can be a sound alternative to assess sustainability, extrapolating its applications to other kinds of problems at different levels of analysis (continents, regions, cities, etc.) further corroborating the effectiveness of the unsupervised training methods

Heuristic design of fuzzy inference systems: a review of three decades of research

This paper provides an in-depth review of the optimal design of type-1 and type-2 fuzzy inference systems (FIS) using five well known computational frameworks: genetic-fuzzy systems (GFS), neuro-fuzzy systems (NFS), hierarchical fuzzy systems (HFS), evolving fuzzy systems (EFS), and multi-objective fuzzy systems (MFS), which is in view that some of them are linked to each other. The heuristic design of GFS uses evolutionary algorithms for optimizing both Mamdani-type and Takagi–Sugeno–Kang-type fuzzy systems. Whereas, the NFS combines the FIS with neural network learning systems to improve the approximation ability. An HFS combines two or more low-dimensional fuzzy logic units in a hierarchical design to overcome the curse of dimensionality. An EFS solves the data streaming issues by evolving the system incrementally, and an MFS solves the multi-objective trade-offs like the simultaneous maximization of both interpretability and accuracy. This paper offers a synthesis of these dimensions and explores their potentials, challenges, and opportunities in FIS research. This review also examines the complex relations among these dimensions and the possibilities of combining one or more computational frameworks adding another dimension: deep fuzzy systems

Medición de Sostenibilidad en tres ciudades del Ecuador, Quito, Guayaquil y Cuenca con aplicación comparativa a las principales ciudades del mundo

Con más del 50% de la población mundial urbana es importante evaluar la sostenibilidad de las ciudades y encontrar formas sistemáticas de mejorarla. El modelo SAFE (evaluación de la sostenibilidad por evaluación difusa), primero para medir la sostenibilidad de los países, se modificó para evaluar la sostenibilidad de las ciudades en el mundo. Sostenibilidad global es una función de dos entradas principales, ecológicas y bienestar. La entrada ecológica depende del estado del aire, la tierra y el agua y la entrada de bienestar en el estado de la economía, la educación, la salud y el medio ambiente cívica de las ciudades. SAFE utiliza 46 insumos básicos para clasificar 104 ciudades de acuerdo con la sostenibilidad. El número de entradas puede cambiar según las necesidades. Un análisis de sensibilidad identifica los indicadores básicos que afectan a la sostenibilidad de la mayoría. Si se mejoran estos insumos, la sostenibilidad de las ciudades mejora más rápido. Así Quito, Guayaquil y Cuenca siendo las tres ciudades más importantes de Ecuador ocupan lugares 45a, 66a, 76a, respectivamente. Los indicadores básicos que mejorar en el Ecuador son la pobreza, el índice de criminalidad, los gastos militares, los espacios verdes y facilidad de hacer negocios, considerando que la sostenibilidad no está completa si no se considera su triple integralidad.With more than 50% of the world's urban population it is important to assess the sustainability of cities and find systematic ways to improve it. The model SAFE (sustainability assessment by fuzzy evaluation), first to measure the sustainability of countries was modified to assess the sustainability of cities in the world. Global sustainability is a function of two main, ecological and welfare entries. The entry depends on the ecological state of the air, land and water and the entry of well-being in the state of the economy, education, health and the civic environment of cities. SAFE uses 46 basic inputs to rank 104 cities according to sustainability. The number of entries can be changed as needed. A sensitivity analysis identifies the key indicators that affect the sustainability of the majority. If these inputs are improved, the sustainability of cities improving faster. So Quito, Guayaquil and Cuenca be the three most important cities of Ecuador occupy places 45a, 66a, 76a, respectively. Poverty, crime rates, military spending, green spaces and ease of doing business are the basic indicators to improve in Ecuador. It is recommended to consider sustainability in tis entirely triple and deepen in a system of indicators for the country reality

Prognóstico de sustentabilidade como apoio à decisão no licenciamento ambiental: desenvolvimento de método utilizando dinâmica de sistemas, lógica fuzzy e backcasting

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-graduação em Engenharia Civil, Florianópolis, 2014Esta tese apresenta o desenvolvimento de um método de previsão da sustentabilidade ambiental de projetos. Este método utiliza a base de dados de estudos de avaliação de impacto ambiental e usa a lógica fuzzy para agregação de informações ambientais e socioeconômicas. O método também simula dinamicamente as mudanças nos sistemas em analise, considerando possíveis cenários futuros construídos sob o enfoque de backcasting. A construção dos cenários futuros é realizada após a definição dos padrões desejados e indesejados pelos órgãos licenciadores considerando as discussões sobre as questões de interesse entre os stakeholders. Partindo-se de cenários tendenciais, idealizam-se cenários futuros possíveis, desejáveis e indesejáveis. As variáveis são agregadas com o uso do software fuzzyTECH® e as simulações das dinâmicas dos cenários são modeladas utilizando o software STELLA®. O método visa o apoio à decisão no licenciamento ambiental e foi testado em uma simulação de licenciamento ambiental de um resort, demonstrando ser adequado para estimar a sustentabilidade ambiental de projetos, pela avaliação de índices locais de sustentabilidade. O método permite a simulação de diversos cenários e habilita a comparação entre os cenários analisados, favorecendo as decisões.Abstract: This thesis presents the developing of a method for predicting the environmental sustainability of projects. This method uses environmental impact assessment database and employs fuzzy logic for the aggregation of environmental and socioeconomic information. The method also simulates dynamically the changes in the analysed systems, considering possible future scenarios constructed under a backcasting approach. The construction of future scenarios is performed after defining the desired and undesired standards by environmental agencies, considering the discussions about the issues of concern among the stakeholders. Starting from emerging scenarios, the possible future scenarios, desirable and undesirable are idealized. Variables are aggregated using the fuzzyTECH® software and the simulations of the dynamics of the scenarios are modelled using the STELLA® software. The method aims at supporting decision-making in environmental licensing and was tested in an environmental licensing simulation of a resort, proving to be suitable for estimating the environmental sustainability of projects, by the evaluation of local sustainability indices. The method allows the simulation of various scenarios and enables comparison among the analyzed scenarios, favouring decisions

On the monotonicity of hierarchical sum–product fuzzy systems

Summarization: Motivated by the authors’ previous work on the control of queueing systems, the assessment of sustainable development, and the measurement of material recyclability, this paper provides sufficient conditions on the parameters of hierarchical fuzzy systems under which the output of the system is monotonic with respect to its inputs. This property could be useful in designing multistage fuzzy inference systems and fuzzy controllers.Παρουσιάστηκε στο: Fuzzy sets and system