Comparison of tracking algorithms for single layer threshold networks in the presence of random drift

Kuh, Anthony

doi:10.1109/78.558480

Cited by 9 publications

(4 citation statements)

References 21 publications

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“…Unfortunately, when the dynamic of the process under investigation is faster than the tracking ability of the JIT classifier, last acquired samples are already obsolete and the JIT classifiers may lead to unsatisfactory results (in feedforward neural-network-based classifiers, we will update weights online, at each instant of time, to cope with such fast evolution, as suggested in [15]). …”

Section: Algorithm 3: Jit Adaptive Classifiersmentioning

confidence: 98%

“…A time adaptive self-organizing map (SOM), automatically adjusting the network parameters to work both in stationary and nonstationary environments, is presented in [11]; Carpenter et al [12] propose a fuzzy ARTMAP as a nonparametric probability estimator for nonstationary pattern recognition problems and [13] a probabilistic neural network for classifying patterns characterized by time-varying distributions. In [14], an online tracking of analog neurons subject to slowly drifting weights is proposed, while Kuh [15] provides a comparison of tracking algorithms for single-layer threshold networks in presence of random drifts.…”

Section: Introductionmentioning

confidence: 99%

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Just-in-Time Adaptive Classifiers—Part II: Designing the Classifier

Alippi

Roveri

2008

IEEE Trans. Neural Netw.

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Aging effects, environmental changes, thermal drifts, and soft and hard faults affect physical systems by changing their nature and behavior over time. To cope with a process evolution adaptive solutions must be envisaged to track its dynamics; in this direction, adaptive classifiers are generally designed by assuming the stationary hypothesis for the process generating the data with very few results addressing nonstationary environments. This paper proposes a methodology based on k-nearest neighbor (NN) classifiers for designing adaptive classification systems able to react to changing conditions just-in-time (JIT), i.e., exactly when it is needed. k-NN classifiers have been selected for their computational-free training phase, the possibility to easily estimate the model complexity k and keep under control the computational complexity of the classifier through suitable data reduction mechanisms. A JIT classifier requires a temporal detection of a (possible) process deviation (aspect tackled in a companion paper) followed by an adaptive management of the knowledge base (KB) of the classifier to cope with the process change. The novelty of the proposed approach resides in the general framework supporting the real-time update of the KB of the classification system in response to novel information coming from the process both in stationary conditions (accuracy improvement) and in nonstationary ones (process tracking) and in providing a suitable estimate of k. It is shown that the classification system grants consistency once the change targets the process generating the data in a new stationary state, as it is the case in many real applications.

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Section: Algorithm 3: Jit Adaptive Classifiersmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Just-in-Time Adaptive Classifiers—Part II: Designing the Classifier

Alippi

Roveri

2008

IEEE Trans. Neural Netw.

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“…The knowledge-based modality assumes instead that some a priori information about the process (but not the model) is available, e.g., derived from data samples based on causal analysis [4] [6]. The identification of the time instant associated with the loss in stationarity allows the designer to take actions, e.g., by updating the classifier network weights to track the process evolution [7] or retraining the classifier [8] [9] exactly when needed.…”

Section: Introductionmentioning

confidence: 99%

Designing a robust type-2 fuzzy logic classifier for non-stationary systems with application in brain-computer interfacing

Herman

Prasad

McGinnity

2008

2008 IEEE International Conference on Systems, Man and Cybernetics

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Type-2 (T2) fuzzy logic (FL) systems (T2FLSs) have shown a remarkable potential in dealing with uncertain data resulting from real-world systems with non-stationary characteristics. This paper reports on novel developments in interval T2FLS (IT2FLS) classifier design methodology so that system non-stationarities can be effectively handled. In general, the approach presented here rests on a general concept of twostage FLS design in which an initial rule base structure is first initialized and then system parameters are globally optimized. The proposed incremental enhancements of existing fuzzy techniques, adopted from the area of conventional type-1 (T1) FL, are heuristic in nature. The IT2FLS design methods have been empirically verified in this work in the realm of pattern recognition. In particular, the potential and the suitability of IT2FLS to the problem of classification of motor imagery (MI) related patterns in electroencephalogram (EEG) recordings has been investigated. The outcome of this study bears direct relevance to the development of EEG-based brain-computer interfaces (BCIs) since the problem under examination poses a major difficulty for the state-of-the-art BCI methods. The IT2FLS classifier is evaluated in this work on multi-session EEG data sets in the framework of an off-line BCI. Its performance isquantified in terms of the classification accuracy (CA) rates and has been found to be favorable to that of analogous systems employing a conventional T1FLS, along with linear discriminant analysis (LDA) and support vector machine (SVM), commonly utilized in MI-based BCI systems.

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“…To make the analysis tractable, this section discusses a number of assumptions that are used in the analysis of Section 4. 0 the drift rate 7 > 0 is small and can be neglected 0 the noise v ( k ) has small variance so that g(s(k) + (see discussion in [4]). …”

Section: Assumptionsmentioning

confidence: 98%

Performance of analog neural networks subject to drifting targets and noise

Kuh

IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339)

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This paper analyzes the tracking behavior of analog neurons when subjected to additive noise and slowly drifting target weights. The performance of single analog neurons and multilayer analog neural networks are studied. We consider a system identification model with a target and tracking neural network. The target weights are described by a stochastic difference equation with weights changing slowly with time. The tracker weights follow the Least Mean Square (LMS) gradient descent algorithm and at each update are given a noise corrupted value of the output of the target network. When inputs are Gaussian and the activation used is the Gaussian error function, g(z) = l/&JTz e-lai2dt. (closely approximates the standard sigmoidal activation function) the analysis is tractable. The dynamies of target and tracking networks are described by a set of stochastic difference equations. We obtain an approximation of the mean squared generalization error by linearizing the nonlinear difference equations and using simple probabilistic arguments. Analytical results are then confirmed with simulation results.

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Comparison of tracking algorithms for single layer threshold networks in the presence of random drift

Cited by 9 publications

References 21 publications

Just-in-Time Adaptive Classifiers—Part II: Designing the Classifier

Just-in-Time Adaptive Classifiers—Part II: Designing the Classifier

Designing a robust type-2 fuzzy logic classifier for non-stationary systems with application in brain-computer interfacing

Performance of analog neural networks subject to drifting targets and noise

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