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This article addresses the robust estimation of the output layer linear parameters in a radial basis function network (RBFN). A prominent method used to estimate the output layer parameters in an RBFN with the predetermined hidden layer parameters is the least-squares estimation, which is the maximum-likelihood (ML) solution in the specific case of the Gaussian noise. We highlight the connection between the ML estimation and minimizing the Kullback-Leibler (KL) divergence between the actual noise distribution and the assumed Gaussian noise. Based on this connection, a method is proposed using a variant of a generalized KL divergence, which is known to be more robust to outliers in the pattern recognition and machine-learning problems. The proposed approach produces a surrogate-likelihood function, which is robust in the sense that it is adaptive to a broader class of noise distributions. Several signal processing experiments are conducted using artificially generated and real-world data. It is shown that in all cases, the proposed adaptive learning algorithm outperforms the standard approaches in terms of mean-squared error (MSE). Using the relative increase in the MSE for different noise conditions, we compare the robustness of our proposed algorithm with the existing methods for robust RBFN training and show that our method results in overall improvement in terms of absolute MSE values and consistency.
This article was published in the following journal.
Name: IEEE transactions on cybernetics
This paper addresses the problem of adaptive neural output-feedback decentralized control for a class of strongly interconnected nonlinear systems suffering stochastic disturbances. An state observer ...
In this paper, the leader-following consensus problem of a class of nonlinearly multi-dimensional multi-agent systems with actuator faults is addressed by developing a novel neural network learning st...
This article investigates the problem of small fault detection (sFD) for discrete-time nonlinear systems with uncertain dynamics. The faults are considered to be ``small'' in the sense that the system...
This paper considers the self-synchronization and tracking synchronization issues for a class of nonidentically coupled neural networks model with unknown parameters and diffusion effects. Using the s...
For state-of-the-art optical elements in laser fusion applications, optical surfaces, especially those with residual fabrication error, are generally complex in symmetry, shape of aperture, and spatia...
Motor learning occurs with structural and functional modifications in neural networks that meet a certain demand. The improvement of performance in diverse activities is a measure of learn...
The purpose of this study is to determine if there is a difference in the size and the depth of the radial artery at the access points for established radial and new distal radial approach...
Traditionally, coronary angiograms are performed through the radial artery which is accessed above the palm of the 'right' hand. In recent years, some cardiologists are performing this pro...
The purpose of this study is to determine if the Ascension PyroCarbon Radial Head is safe and effective in the treatment of arthritis, fractures, symptoms from radial head resections, and ...
All the studies underlined the high frequency of co-morbid associations in specific learning disorders. Understanding the reasons for these associations could enable us to determine the ce...
Process in which individuals take the initiative, in diagnosing their learning needs, formulating learning goals, identifying resources for learning, choosing and implementing learning strategies and evaluating learning outcomes (Knowles, 1975)
A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming.
Behavioral treatment that uses drill and practice, compensatory and adaptive strategies to facilitate improvement in targeted learning areas.
Disease involving the RADIAL NERVE. Clinical features include weakness of elbow extension, elbow flexion, supination of the forearm, wrist and finger extension, and thumb abduction. Sensation may be impaired over regions of the dorsal forearm. Common sites of compression or traumatic injury include the AXILLA and radial groove of the HUMERUS.
A procedure to surgically correct REFRACTIVE ERRORS by cutting radial slits into the CORNEA to change its refractive properties.