André Paim Lemos

Multivariable Gaussian Evolving Fuzzy Modeling System

This paper introduces a class of evolving fuzzy rule-based system as an approach for multivariable Gaussian adaptive fuzzy modeling. The system is an evolving Takagi-Sugeno (eTS) functional fuzzy model, whose rule base can be continuously updated using a new recursive clustering algorithm based on participatory learning. The fuzzy sets of the rule antecedents are multivariable Gaussian membership functions, which have been adopted to preserve information between input variable interactions. The parameters of the membership functions are estimated by the clustering algorithm. A weighted recursive least-squares algorithm updates the parameters of the rule consequents. Experiments considering time-series forecasting and nonlinear system identification are performed to evaluate the performance of the approach proposed. The multivariable Gaussian evolving fuzzy models are compared with alternative evolving fuzzy models and classic models with fixed structures. The results suggest that multivariable Gaussian evolving fuzzy modeling is a promising approach for adaptive system modeling.

Adaptive fault detection and diagnosis using an evolving fuzzy classifier

This paper suggests an approach for adaptive fault detection and diagnosis. The proposed approach detects new operation modes of a process such as operation point changes and faults, and incorporates information about operation modes in an evolving fuzzy classifier used for diagnosis. The approach relies upon an incremental clustering procedure to generate fuzzy rules describing new operational states detected. The classifier performs diagnostic adaptively and, since every new operation mode detected is learnt and incorporated into the classifier, it is capable of identifying the same operation mode the next time it occurs. The efficiency of the approach is verified in fault detection and diagnosis of an industrial actuator. Experimental results suggest that the approach is a promising alternative for fault diagnosis of dynamic systems when there is no a priori information about all failure modes, and as an alternative to incremental learning of diagnosis systems using data streams.

A fast learning algorithm for evolving neo-fuzzy neuron

This paper introduces an evolving neural fuzzy modeling approach constructed upon the neo-fuzzy neuron and network. The approach uses an incremental learning scheme to simultaneously granulate the input space and update the neural network weights. The neural network structure and parameters evolve simultaneously as data are input. Initially the space of each input variable is granulated using two complementary triangular membership functions. New triangular membership functions may be added, excluded and/or have their parameters adjusted depending on the input data and modeling error. The parameters of the network are updated using a gradient-based scheme with optimal learning rate. The performance of the approach is evaluated using instances of times series forecasting and nonlinear system identification problems. Computational experiments and comparisons against alternative evolving models show that the evolving neural neo-fuzzy network is accurate and fast, characteristics which are essential for adaptive systems modeling, especially in real-time, on-line environments.

Fuzzy evolving linear regression trees

This paper introduces a new approach for evolving fuzzy modeling using tree structures. The model is a fuzzy linear regression tree whose topology can be continuously updated through a statistical model selection test. A fuzzy linear regression tree is a fuzzy tree with linear model in each leaf. An incremental learning algorithm approach evolves the tree replacing leaves with subtrees that improve the model quality. The learning algorithm evaluates each input only once and do not need to store any past values. The evolving linear regression model is evaluated using time series forecasting problems. The performance is compared against alternative evolving fuzzy models and classic models with fixed structures. The results suggest that fuzzy evolving regression tree is a promising approach for adaptive system modeling.

New uninorm-based neuron model and fuzzy neural networks

This paper suggests a uninorm-based neuron model and a neural network architecture using unineurons. The unineuron generalizes logical and/or neurons using weighted uninorms. Previous works have addressed fuzzy neurons within the framework of uninorms. This paper introduces a new unineuron model that uses weighted aggregation of the inputs, and computes its output using a conventional neuron. A feedforward fuzzy neural architecture is developed and used to model nonlinear dynamic systems. The resulting fuzzy neural network easily allows fuzzy rule insertion and/or extraction from its topology, process information following a fuzzy inference mechanism, and is an universal function approximator. Experimental results show that the uninorm-based network provides accurate results and performs better than several similar neural and alternative fuzzy function approximators.

Fuzzy multivariable Gaussian evolving approach for fault detection and diagnosis

This paper suggests an approach for fault detection and diagnosis capable to detect new operation modes online. The approach relies upon an evolving fuzzy classifier able to incorporate new operational information using an incremental unsupervised clustering procedure. The efficiency of the approach is verified in fault detection and diagnosis of an induction machine. Experimental results suggest that the approach is a promising alternative for fault diagnosis of dynamic systems when there is no a priori information about all failure modes. It is also attractive for incremental learning of diagnosis systems with streams of data.

Evolving Neo-fuzzy Neural Network with Adaptive Feature Selection

This paper suggests an approach to develop a class of evolving neural fuzzy networks with adaptive feature selection. The approach uses the neo-fuzzy neuron structure in conjunction with an incremental learning scheme that, simultaneously, selects the input variables, evolves the network structure, and updates the neural network weights. The mechanism of the adaptive feature selection uses statistical tests and information about the current model performance to decide if a new variable should be added, or if an existing variable should be excluded or kept as an input. The network structure evolves by adding or deleting membership functions and adapting its parameters depending of the input data and modeling error. The performance of the evolving neural fuzzy network with adaptive feature selection is evaluated considering instances of times series forecasting problems. Computational experiments and comparisons show that the proposed approach is competitive and achieves higher or as high performance as alternatives reported in the literature.

Evolving fuzzy linear regression trees with feature selection

This paper introduces an approach to evolve fuzzy modeling that simultaneously performs adaptive feature selection. The model is a fuzzy linear regression tree whose topology can be continuously updated using statistical tests. A fuzzy linear regression tree is a fuzzy tree with linear model in each leaf. The number of tree nodes and the number of inputs can be updated for each new input. The precision and the feature selection mechanism of the proposed model are evaluated using system identification and time series forecasting problems. The results suggest that the evolving tree model is a promising approach for adaptive system modeling with feature selection.

Real-time nonlinear modeling of a twin rotor MIMO system using evolving neuro-fuzzy network

This paper presents an evolving neuro-fuzzy network approach (eNFN) to model a twin rotor MIMO system (TRMS) with two degrees of freedom in real-time. The TRMS is a fast, nonlinear, open loop unstable time-varying dynamic system, with cross coupling between the rotors. Modeling and control of TRMS require high sampling rates, typically in the order of milliseconds. Actual laboratory implementation shows that eNFN is fast, effective, and accurately models the TRMS in real-time. The eNFN captures the TRMS system dynamics quickly, and develops precise low cost models from the point of view of time and space complexity. The results suggest eNFN as a potential candidate to model complex, fast time-varying dynamic systems in real-time.

Thermal modeling of power transformers using evolving fuzzy systems

Thermal models for distribution transformers with core immersed in oil are of utmost importance for transformers lifetime study. The hot spot temperature determines the degradation speed of the insulating paper. High temperatures cause loss of mechanical stiffness, generating failures. Since the paper is the most fragile component of the transformer, its degradation determines the lifetime limits. Thus, good thermal models are needed to generate reliable data for lifetime forecasting methodologies. It is also desired that thermal models are able to adapt to cope with changes in the transformer behavior due to structural changes, maintenance and so on. In this work we apply an evolving fuzzy model to build adaptive thermal models of distribution transformers. The model used is able to adapt its parameters and also its structure based on a stream of data. The proposed model is evaluated using actual data from an experimental transformer. The results suggest that evolving fuzzy models are a promising approach for adaptive thermal modeling of distribution transformers.