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Because of the disturbance of operation environment in mass rapid transit (MRT) system, the robustness against disturbance and the schedule punctuality under control constraint are important issues to be considered in designing Automatic Train Regulation (ATR) for MRT system. In this paper, the study on suitable traffic model for designing ATR system and ATR design based on adaptive critic design...
Although supervised learning has been widely used to tackle problems of function approximation and regression estimation, prior knowledge fails to be incorporated into the data-driven approach because the form of input-output data pairs are not applied. To overcome this limitation, focusing on the fusion between rough fuzzy system and very rare samples of input-output pairs with noise, this paper...
The research on unsupervised feature selection is scarce in comparison to that for supervised models, despite the fact that this is an important issue for many clustering problems. An unsupervised feature selection method for general Finite Mixture Models was recently proposed and subsequently extended to generative topographic mapping (GTM), a manifold learning constrained mixture model that provides...
Exploratory activities seem to be crucial for our cognitive development. According to psychologists, exploration is an intrinsically rewarding behaviour. The developmental robotics aims to design computational systems that are endowed with such an intrinsic motivation mechanism. There are possible links between developmental robotics and machine learning. Affective computing takes into account emotions...
Estimation of plant Jacobian is necessary for successful control of nonlinear systems using neural networks with the specialized learning scheme. Our previous study showed that neuro-emulators provide a better estimation of the plant Jacobian using a new cost function for learning during the course of dynamic modeling and control. This paper presents an approach for further enhancing the estimation...
The neural extended Kalman filter (NEKF) is an adaptive state estimation technique that can be used in target tracking and directly in a feedback loop. It improves state estimates by learning the difference between the a priori model and the actual system dynamics. The neural network training occurs while the system is in operation. Often, however, due to stability concerns, such an adaptive component...
The paper proposed to use recurrent fuzzy-neural multi-model (FNMM) identifier for decentralized identification of a distributed parameter anaerobic wastewater treatment digestion bioprocess, carried out in a fixed bed and a recirculation tank. The distributed parameter analytical model of the digestion bioprocess is reduced to a lumped system using the orthogonal collocation method, applied in three...
This paper investigates the possibility of a pseudo-online adaptive training schema for Mamdani-type neuro-fuzzy models that have robust linguistic interpretability. As such verbatim models are incapable of complex constructs available to Takagi-Sugeno-type neuro-fuzzy models, a heuristic approach is developed to allow the rule bases to adapt accordingly to fundamental shifts in the characteristics...
In this paper, we propose a new type of neural adaptive control via dynamic neural networks. For a class of unknown nonlinear systems, a neural identifier-based feedback linearization controller is first used. Dead-zone and projection techniques are applied to assure the stability of neural identification. Then four types of compensator are addressed. The stability of closed-loop system is also proven.
This paper proposes an approach to learn subject-independent P300 models for EEG-based brain-computer interfaces. The P300 models are first learned using a pool of existing subjects and Fisher linear discriminant, and then autonomously adapted to the unlabeled data of a new subject using an unsupervised machine learning technique. In data analysis, we apply this technique to a set of EEG data of 10...
Pattern classification is an important task in speech recognition and speaker verification. Given the feature vectors of an input the goal is to capture the characteristics of these features unique to each class. This paper deals with exploring Auto Associative Neural Network (AANN) models for the task of speaker verification and speech recognition. We show that AANN models produce comparable performance...
In this study, an adaptive output recurrent cerebellar model articulation controller (AORCMAC) is investigated to control the two-wheeled robot. The main purpose is to develop a self-dynamic balancing and motion control strategy. The proposed AORCMAC has superior capability to the conventional cerebellar model articulation controller in efficient learning mechanism and dynamic response. The dynamic...
In this paper, we describe an adaptive technique for states and parameter estimation involving a combination of two methods, namely the Variable Structure Filter (VSF) and the Extend Kalman Filters (EKF).
This work focuses on a study about hybrid optimization techniques for improving feature selection and weighting applications. For this purpose, two global optimization methods were used: Tabu search (TS) and simulated annealing (SA). These methods were combined to k-nearest neighbor (k-NN) composing two hybrid approaches: SA/k-NN and TS/k-NN. Those approaches try to use the main advantage from the...
Model field theory (MFT) is a powerful tool of pattern recognition, which has been used successfully for various tasks involving noisy data and high level of clutter. Detection of spatio-temporal activity patterns in EEG experiments is a very challenging task and it is well-suited for MFT implementation. Previous work on applying MFT for EEG analysis used Gaussian assumption on the mixture components...
In neural network modeling, the goal often is to get a most specific crisp output (e.g., binary classification of objects) from neuron inputs that have multiple possible values. In this paper, we change the viewpoint and assume that the neuron is an operator that transforms binary classical logic input to the many valued logic output, e.g., changes crisp sets into fuzzy sets. In this interpretation,...
e-Learning is a critical support mechanism for industrial and academic organizations to enhance the skills of employees and students and, consequently, the overall competitiveness in the new economy. The remarkable velocity and volatility of modern knowledge require novel learning methods offering additional features as efficiency, task relevance and personalization. The main aim of adaptive eLearning...
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