Pyramo Costa

Evolving fuzzy granular modeling from nonstationary fuzzy data streams

Evolving granular modeling is an approach that considers online granular data stream processing and structurally adaptive rule-based models. As uncertain data prevail in stream applications, excessive data granularity becomes unnecessary and inefficient. This paper introduces an evolving fuzzy granular framework to learn from and model time-varying fuzzy input–output data streams. The fuzzy-set based evolving modeling framework consists of a one-pass learning algorithm capable to gradually develop the structure of rule-based models. This framework is particularly suitable to handle potentially unbounded fuzzy data streams and render singular and granular approximations of nonstationary functions. The main objective of this paper is to shed light into the role of evolving fuzzy granular computing in providing high-quality approximate solutions from large volumes of real-world online data streams. An application example in weather temperature prediction using actual data is used to evaluate and illustrate the usefulness of the modeling approach. The behavior of nonstationary fuzzy data streams with gradual and abrupt regime shifts is also verified in the realm of the weather temperature prediction.

Evolving granular neural networks from fuzzy data streams

This paper introduces a granular neural network framework for evolving fuzzy system modeling from fuzzy data streams. The evolving granular neural network (eGNN) is able to handle gradual and abrupt parameter changes typical of nonstationary (online) environments. eGNN builds interpretable multi-sized local models using fuzzy neurons for information fusion. An online incremental learning algorithm develops the neural network structure from the information contained in data streams. We focus on trapezoidal fuzzy intervals and objects with trapezoidal membership function representation. More precisely, the framework considers triangular, interval, and numeric types of data to construct granular fuzzy models as particular arrangements of trapezoids. Application examples in classification and function approximation in material and biomedical engineering are used to evaluate and illustrate the neural network usefulness. Simulation results suggest that the eGNN fuzzy modeling approach can handle fuzzy data successfully and outperforms alternative state-of-the-art approaches in terms of accuracy, transparency and compactness.

Evolving granular neural network for semi-supervised data stream classification

In this paper we introduce an adaptive fuzzy neural network framework for classification of data stream using a partially supervised learning algorithm. The framework consists of an evolving granular neural network capable of processing nonstationary data streams using a one-pass incremental algorithm. The granular neural network evolves fuzzy hyperboxes and uses nullnorm based neurons to classify data. The learning algorithm performs structural and parametric adaptation whenever environment changes are reflected in input data. It needs no prior statistical knowledge about data and classes. Computational experiments show that the fuzzy granular neural network is robust against different types of concept drift, and is able to handle unlabeled examples efficiently.

Fuzzy granular evolving modeling for time series prediction

Modeling large volumes of flowing data from complex systems motivates rethinking several aspects of the machine learning theory. Data stream mining is concerned with extracting structured knowledge from spatio-temporally correlated data. A profusion of systems and algorithms devoted to this end has been constructed under the conceptual framework of granular computing. This paper outlines a fuzzy set based granular evolving modeling — FBeM — approach for learning from imprecise data. Granulation arises because modeling uncertain data dispenses attention to details. The evolving aspect is fundamental to account endless flows of nonstationary data and structural adaptation of models. Experiments with classic Box-Jenkins and Mackey-Glass benchmarks as well as with actual Global40 bond data suggest that the FBeM approach outperforms alternative approaches.

Interval Approach for Evolving Granular System Modeling

Physical systems change over time and usually produce considerable amount of nonstationary data. Evolving modeling of time-varying systems requires adaptive and flexible procedures to deal with heterogeneous data. Granular computing provides a rich framework for modeling time-varying systems using nonstationary granular data streams. This work considers interval granular objects to accommodate essential information from data streams and simplify complex real-world problems. We briefly discuss a new class of problems emerging in data stream mining where data may be either singular or granular. Particularly, we emphasize interval data and interval modeling framework. Interval-based evolving modeling (IBeM) approach recursively adapts both parameters and structure of rule-based models. IBeM uses ∪-closure granular structures to approximate functions. In general, approximand functions can be time series, decision boundaries between classes, control, or regression functions. Essentially, IBeM accesses data sequentially and discards previous examples; incoming data may trigger structural adaptation of models. The IBeM learning algorithm evolves and updates rules quickly to track system and environment changes. Experiments using heterogeneous streams of meteorological and financial data are performed to show the usefulness of the IBeM approach in actual scenarios.

Participatory Learning in Power Transformers Thermal Modeling

In this paper, we introduce a new approach based on the participatory learning paradigm to train a class of hybrid neurofuzzy networks whose aim is to model the thermal behavior of power transformers. The participatory learning paradigm is a training procedure that tends to emulate the human learning mechanism. An acceptance mechanism determines which observation is used for learning based upon their compatibility with the current beliefs. The proposed model is compared with actual data obtained from an experimental power transformer equipped with fiber-optic probes. Comparisons with alternative approaches suggested in the literature are included to show the effectiveness of participatory learning to model the thermal behavior of power transformers.

Granular approach for evolving system modeling

In this paper we introduce a class of granular evolving system modeling approach within the framework of interval analysis. Our aim is to present an interval-based learning algorithm which develops both, granular and singular approximations of nonlinear nonstationary functions using singular data. The algorithm is capable of incrementally creating/adapting both model parameters and structure. These are key features in nonlinear systems modeling. In addition, interval analysis provides rigorous bounds on approximation errors, rounding errors, and on uncertainties in data propagated during computations. The learning algorithm is simple and particularly suited to process stream of data in real time. In this paper we focus on the foundations of the approach and on the details of the learning algorithm. An application concerning economic time series forecasting illustrates the usefulness and efficiency of the approach.

Interval-based evolving modeling

This paper introduces a granular, interval-based evolving modeling (IBeM) approach to develop system models from a stream of data. IBeM is an evolving rule-based modeling scheme that gradually adapts its structure (information granules and rule base) and rules antecedent and consequent parameters from data (inductive learning). Its main purpose is continuous learning, self-organization, and adaptation to unknown environments. The IBeM approach develops global model of a system using a fast, one-pass learning algorithm, and modest memory requirements. To illustrate the effectiveness of the approach, the paper considers actual time series forecasting applications concerning electricity load and stream flow forecasting.

Recurrent Neurofuzzy Network in Thermal Modeling of Power Transformers

This work suggests recurrent neurofuzzy networks as a means to model the thermal condition of power transformers. Experimental results with actual data reported in the literature show that neurofuzzy modeling requires less computational effort, and is more robust and efficient than multilayer feedforward networks, a radial basis function network, and classic deterministic modeling approaches

Evolving granular classification neural networks

The objective of this study is to introduce the concept of evolving granular neural networks (eGNN) and to develop a framework of information granulation and its role in the online design of neural networks. The suggested eGNN are neural models supported by granule-based learning algorithms whose aim is to tackle classification problems in continuously changing environments. eGNN are constructed from streams of data using fast incremental learning algorithms. eGNN models require a relatively small amount of memory to perform classification tasks. Basically, they try to find information occurring in the incoming data using the concept of granules and T-S neurons as basic processing elements. The main characteristics of eGNN models are continuous learning, self-organization, and adaptation to unknown environments. Association rules and parameters can be easily extracted from its structure at any step during the evolving process. The rule base gives a granular description of the behavior of the system in the input space together with the associated classes. To illustrate the effectiveness of the approach, the paper considers the Iris and Wine benchmark problems.