Heloisa A. Camargo

Fuzzy timed Petri nets

Abstract The study is concerned with a new temporal version of fuzzy Petri nets—fuzzy timed Petri nets (ftPNs). These nets are augmented by temporal fuzzy sets that allow for the representation of timing effect (e.g., aging of information). We show how the time factor can be added as an integral part of the models of transitions and places. The formalism of the extended net is introduced and studied in depth. A series of illustrative examples are presented with intent of developing a better qualitative insight into the temporal behavior of the nets. A detailed learning scheme for the net is also given.

The use of fuzzy decision trees for coffee rust warning in Brazilian crops

This paper proposes the use of fuzzy decision trees for coffee rust warning, the most economically important coffee disease in the world. The models were induced using field data collected during 8 years. Using different subsets of attributes from the original data, three distinct datasets were constructed. The class attribute, representing the monthly infection rate, was used to construct six datasets according to two distinct infection rates. Induced models can be used to trigger alerts when estimated monthly disease infection rates reach one of the two thresholds. The first threshold allows applying preventive actions, whereas the second one requires a curative action. The fuzzy decision tree models were compared to the ones induced by a classic decision tree algorithm, taking into account the accuracy and the syntactic complexity of the models, as well as its quality according to an expert opinion. The fuzzy models showed better accuracy power and interpretability.

Genetic design of fuzzy knowledge bases - a study of different approaches

The objective of this work is to design, implement and test two different genetic fuzzy systems approaches with the purpose of analyzing the performance of both when applied to classification problems. In the first approach the fuzzy sets are defined previously by fuzzy clustering and the rule base is automatically generated and optimized using genetic algorithms. In the second approach the data base is the object of genetic algorithm learning, instead of the rule base. In this case, the rule base is generated by means of an auxiliary method (Wang & Mendell). Investigations of both methods developed earlier by the authors are described and then, the results of the comparison experiments performed in the present work are presented. The methods have been selected for investigation with the objective of analyzing the performance and the size of the resulting knowledge bases generated through genetic algorithms applied to different KB components.

On the use of fuzzy rules to text document classification

This work presents the integration of a fuzzy method and text mining to obtain an approach that enables the text documents classification to be closer to the user needs. The aim of this work is to develop a mechanism to reduce the high dimensionality of the attribute-value matrix obtained from the documents and, with this, to manage the imprecision and uncertainty using fuzzy rules to classify text documents. Some experiments have been run using different domains in order to validate the proposed approach and to compare the results with the ones obtained with the Ibk, J48, Naive Bayes and OneR classification methods. The advantages of the method, the experiments and the results obtained are discussed.

Genetic generation of fuzzy systems with rule extraction using formal concept analysis

Fuzzy classification systems have been widely researched with many approaches proposed in the literature. Several methods are available for the automatic definition of fuzzy classification systems, which basically comprehend two tasks: i) the definition of the attributes in terms of fuzzy sets, and ii) the generation of a rule set containing the domain knowledge, named fuzzy rule base. Genetic Fuzzy Systems are used to learn or tune in fuzzy classification systems. Some genetic approaches for learning the fuzzy rule base require the previous extraction of a set of rules to be used as the genetic search space. In this paper, we present the FCA-Based method, a proposal for the automatic generation of fuzzy rule bases, which extracts a set of rules using the formal concept analysis theory directly from data. After extracting the rules forming the genetic search space, FCA-Based uses a genetic algorithm to select the final rule base. The last step of the FCA-Based method is a rule pruning step in order to improve the interpretability of the fuzzy rule bases. The extraction of rules proposed for the FCA-Based algorithm presents polynomial complexity and does not require the predefinition of the number of rules to be extracted. As it extracts rules directly from data, the proposed method avoids the random extraction of rules. It also presents the advantage of automatically extracting rules with variable number of conditions in their antecedents. A feature subset selection method, specifically designed for fuzzy classification systems, is integrated into the FCA-Based method in order to reduce the search space of solutions. The FCA-Based method is detailed and compared to eight different rule-based fuzzy systems. Experimental results using 27 benchmark datasets and a 10-fold cross-validation strategy show that FCA-Based presents higher accuracy and statistically significant difference with seven of the eight compared methods.

A modular modeling approach for automated manufacturing systems based on shared resources and process planning using Petri nets

The modeling of automated manufacturing systems has been studied to cope with production planning and control problems. Petri nets have been applied to model these systems because they provide resources to represent the system behavior. When the systems are too large or complex, the modeling task is difficult and the elements in the model are too many for a simple understanding and analysis. This paper proposes an approach to modeling these systems to minimize these difficulties. The proposed approach uses Petri nets and a modular strategy considering shared resources and alternative process planning. Using this approach, a system has been modeled. First the system elements for the modeling job were considered, and then the modules were linked to build the complete model.

A modular modeling approach for CNC machines control using Petri nets

Machine control can be executed in an integrated way using computerized numeric control (CNC) and the programmable logical controller (PLC). CNC deals with axes positioning and speed, the part cut off sequence and the operator interface, while PLC deals with machine interlocking, emergency sequences, start and stop sequences, among others, considering its environment integration. The machine PLC control can be divided into interlocking and sequencing control functions. The article proposes a modular machine PLC control modeling approach using Petri nets. This approach also considers a risk analysis task that defines and classifies the hardware and software interlocking to be implemented to avoid loss and damage. In this approach, the machine PLC control model can be constructed and the Petri net analysis technique can be used to verify and validate it.

Preprocessing in Fuzzy Time Series to Improve the Forecasting Accuracy

The preprocessing in fuzzy time series has an important role to improve the forecast accuracy. The definitions of domain, number of linguistic terms and of the membership function to each fuzzy set, has direct influence in the forecast results. Thus, this paper has the focus on definition of these parameters, before of performing the prediction. The experimental results in enrollments time series show that, when the forecast is performed after proposed preprocessing, the accuracy rate is improved.

A FML-based hybrid reasoner combining fuzzy ontology and Mamdani inference

Fuzzy ontologies have been employed to represent and reason over fuzzy information, which often occurs in real-world applications. Fuzzy inference systems (FIS) are well-known computational intelligence systems whose inferences can also be exploited in fuzzy ontology-based applications. Specifically, the combination of fuzzy ontologies and Mamdani-type FIS can provide inferences involving fuzzy rules and numerical property values, which can be considered in other fuzzy ontology reasoning tasks. In this sense, this paper proposes a hybrid reasoner combining fuzzy ontology and Mamdani inference to provide meaningful inferences that are not available to fuzzy ontology-based applications in an integrated way. Fuzzy rules are represented with Fuzzy Markup Language, providing an abstraction level with regard to the underlying FIS implementation. Some experiments are presented regarding a recommender system context, including a comparison with a fuzzy description logic reasoner in terms of fuzzy rule reasoning semantics and integration issues.

Multiobjective genetic generation of fuzzy classifiers using the iterative rule learning

In this paper, we propose a multiobjective genetic method to learn fuzzy rules and optimize fuzzy sets in Fuzzy Rule Based Classification Systems (FRBCSs) aiming at finding a balance between the accuracy and interpretability objectives. The proposed method comprises three sequential stages: Data Base definition, Rule Base Learning and Data Base Optimization. The two objectives considered are related to the accuracy and interpretability. In the rule generation phase, which adopts the iterative rule learning approach, the accuracy objective is measured by the error rate in classification and the interpretability objective is defined as the number of conditions in the rules. In the second phase, the accuracy objective is defined as the error rate and the interpretability objective is evaluated by a concept of semantic interpretability of fuzzy sets. The second and third stages have been implemented in two versions, inspired on the two well-known techniques of multiobjective optimization: Non-dominated Sorting Genetic Algorithm (NSGA-II) and Strength Pareto Evolutionary Algorithm (SPEA2). The proposed method was compared with other genetic methods that learn the rule base and optimize fuzzy sets found in the literature, and the results showed that our method performs better than the other ones, concerning the accuracy objective while maintaining similar number of rules and conditions.