Purificación Cariñena

A framework for fuzzy quantification models analysis

A framework for description of fuzzy quantification models is presented. Within this framework, the fuzzy quantified statements evaluation problem is described as the compatibility between the fuzzy quantifier and a fuzzy cardinality or a fuzzy aggregation measure. A list of desirable properties for quantification models is presented and those models that fit the framework are confronted with it.

Fuzzy temporal rules for mobile robot guidance in dynamic environments

The paper describes a fuzzy control system for the avoidance of moving objects by a robot. The objects move with no type of restriction, varying their velocity and making turns. Due to the complex nature of this movement, it is necessary to realize temporal reasoning with the aim of estimating the trend of the moving object. A new paradigm of fuzzy temporal reasoning, which we call fuzzy temporal rules (FTRs), is used for this control task. The control system has over 117 rules, which reflects the complexity of the problem to be tackled. The controller has been subjected to an exhaustive validation process and examples are shown of the results obtained.

Landmark detection in mobile robotics using fuzzy temporal rules

Detection of landmarks is essential in mobile robotics for navigation tasks like building topological maps or robot localization. Doors are one of the most common landmarks since they show the topological structure of indoor environments. In this paper, the novel paradigm of fuzzy temporal rules is used for detecting doors from the information of ultrasound sensors. This paradigm can be used both to model the necessary knowledge for detection and to consider the temporal variation of several sensor signals. Experimental results using a Nomad 200 mobile robot in a real environment produce 91% of doors were correctly detected, which show the reliability and robustness of the system.

Voting-model based evaluation of fuzzy quantified sentences: a general framework

The framework here presented allows the definition of new probabilistic methods for fuzzy quantification and also the description of previous ones. All of these methods are endowed with a clear semantic interpretation that is based on a voting model. The methods within the framework fulfil a number of important and adequate properties of interest for fuzzy quantification and can also deal with very different types of quantifiers, as comparative and exception ones.

Fuzzy temporal rules: a rule-based approach for fuzzy temporal knowledge representation and reasoning

In this paper a model for the representation and execution of a type of fuzzy control rules that explicitly handle time variable (Fuzzy Temporal Rules) is presented. The model here described permits using temporal references for the occurrence of events. These references are allowed to be described either in an absolute manner or related to occurrence of other events. A complete grammar that formally describes the representation language of the model is presented, together with its semantic counterpart. This knowledge and reasoning representation model is illustrated by means of a number of examples that show the substantial increase in the expresiveness that FTRs exhibit when compared to usual fuzzy control rules.

Discovering metric temporal constraint networks on temporal databases

OBJECTIVE In this paper, we propose the ASTPminer algorithm for mining collections of time-stamped sequences to discover frequent temporal patterns, as represented in the simple temporal problem (STP) formalism: a representation of temporal knowledge as a set of event types and a set of metric temporal constraints among them. To focus the mining process, some initial knowledge can be provided by the user, also expressed as an STP, that acts as a seed pattern for the searching procedure. In this manner, the mining algorithm will search for those frequent temporal patterns consistent with the initial knowledge. BACKGROUND Health organisations demand, for multiple areas of activity, new computational tools that will obtain new knowledge from huge collections of data. Temporal data mining has arisen as an active research field that provides new algorithms for discovering new temporal knowledge. An important point in defining different proposals is the expressiveness of the resulting temporal knowledge, which is commonly found in the bibliography in a qualitative form. METHODOLOGY ASTPminer develops an Apriori-like strategy in an iterative algorithm where, as a result of each iteration i, a set of frequent temporal patterns of size i is found that incorporates three distinctive mechanisms: (1) use of a clustering procedure over distributions of temporal distances between events to recognise similar occurrences as temporal patterns; (2) consistency checking of every combination of temporal patterns, which ensures the soundness of the resultant patterns; and (3) use of seed patterns to allow the user to drive the mining process. RESULTS To validate our proposal, several experiments were conducted over a database of time-stamped sequences obtained from polysomnography tests in patients with sleep apnea-hypopnea syndrome. ASTPminer was able to extract well-known temporal patterns corresponding to different manifestations of the syndrome. Furthermore, the use of seed patterns resulted in a reduction in the size of the search space, which reduced the number of possible patterns from 2.1×10⁷ to 1219 and reduced the number of frequent patterns found from 1167 to 340, thereby increasing the efficiency of the mining algorithm. CONCLUSIONS A temporal data mining technique for discovering frequent temporal patterns in collections of time-stamped event sequences is presented. The resulting patterns describe different and distinguishable temporal arrangements among sets of event types in terms of repetitive appearance and similarity of the dispositions between the same events. ASTPminer allows users to participate in the mining process by introducing domain knowledge in the form of a temporal pattern using the STP formalism. This knowledge constrains the search to patterns consistent with the provided pattern and improves the performance of the procedure.

A data mining algorithm for inducing temporal constraint networks

A new approach to the problem of temporal knowledge induction from a collection of temporal events is presented. As a result, a set of frequent temporal patterns is obtained, represented following the Simple Temporal Problem (STP) formalism: a set of event types and a set of constraints describing common temporal arrangements between the events. The use of a clustering technique makes it possible to discriminate between the frequent patterns that are found in the collection.

Enhanced fuzzy temporal rules and their projection onto fuzzy Petri nets

In this paper, an enhanced model of fuzzy temporal knowledge bases (FTKBs) formed by fuzzy temporal rules is introduced, and its projection onto the formalism of fuzzy Petri nets (FPNs) is presented. The main objective is to enhance the expressiveness of the rules making up the FTKB, regarding a previous proposal,at the same time as keeping the computation cost within the limits that allow us to work on‐line in dynamic environments. This is attained by means of a partial compaction of the rules, prior to their on‐line execution. The projection of the FTKB over an FPN makes its design, analysis, and execution easier, by explicitly bringing out all its variables and relationships. © 1999 John Wiley & Sons, Inc.

Petri net representation of fuzzy reasoning under incomplete information

In this paper we present an algorithm that performs reasoning processes on fuzzy knowledge bases with chaining between rules. The algorithm we propose permits obtaining valid inferences even in situations where part of the variables in the knowledge base are unknown. The support for the description of the execution algorithm is provided by the Petri net formalism, which organizes in a convenient way all the information in the base, and expresses in a simple way this and other processes performed onto it. This simplicity is mainly achieved also because the execution of the fuzzy knowledge base is carried out in a parameterized truth space using the linguistic truth values defined by J.F. Baldwin (1979), which reduces the computational cost of the process and makes easier its mapping onto the Petri net formalism.

Mining temporal constraint networks by seed knowledge extension

This paper proposes an algorithm for discovering temporal patterns, represented in the Simple Temporal Problem (STP) formalism, that frequently occur in a set of temporal sequences. To focus the search, some initial knowledge can be provided as a seed pattern by a domain expert: the mining process will find those frequent temporal patterns consistent with the seed. The algorithm has been tested on a database of temporal events obtained from polysomnography tests in patients with Sleep Apnea-Hypopnea Syndrome (SAHS).