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In this paper, we present a novel approach for Remaining Useful Life (RUL) estimation problem in prognostics using a proposed ‘sequential learning Meta-cognitive Regression Neural Network (McRNN) algorithm for function approximation’. The McRNN has two components, namely, a cognitive component and a meta-cognitive components. The cognitive component is an evolving single hidden layer Radial Basis...
This paper presents an approach for automatic diagnosis of Autism Spectrum Disorder (ASD) among males using functional Magnetic Resonance Imaging (fMRI). fMRI has the capability to identify any abnormal neural interactions that may be responsible for behavioral symptoms observed in ASD patients. In this paper, the regional homogeneity of the voxels in the 116 regions of the automated anatomical labeling...
Attention Deficit Hyperactivity Disorder (ADHD) is one of the widely researched neuro-developmental disorder. This paper highlights the importance of phenotypic information in the diagnosis of ADHD, in addition to Magnetic Resonance Image (MRI) based features. In this study, features from amygdala region of the brain is extracted using region of interest based feature extraction technique. These features...
In this paper, a new meta-cognitive RBF neural network classifier that uses a q-Gaussian activation function is presented. The q-Gaussian activation function has the capability to extend or contract the shape/response of the radial basis activation function, based on the value of the parameter q. This property is used to avoid a sharp fall in the response in the tail region, particularly when the...
In this paper, we propose a modified Meta-Cognitive Radial Basis Function Network (McRBFN+) and its Projection Based Learning (PBL) algorithm for classification problems. During learning, as each sample is presented to McRBFN+, the modified meta-cognitive component monitors the prediction error and class-wise significance in cognitive component (RBFN) to efficiently decide on what-to-learn, when-to-learn...
Attention Deficiency Hyperactivity Disorder (ADHD) as a disruptive behavior disorder is receiving lots of attention because of its complexity and need for early detection. This paper presents a study on identification of potential biomarkers in the diagnosis of ADHD based on the structural-MRI of the brain obtained through ADHD-200 competition data set. The region of the brain considered here is "hippocampus"...
In this paper, we present an efficient learning algorithm for a Fully Complex-valued Radial Basis Function (FC-RBF) Network using a self-regulatory system. One of the important issues in gradient descent learning algorithm for complex-valued network is the proper selection of training data sequence. In general, it is assumed that the training data is uniformly distributed in the input space with non-recurrent...
Beamforming is an array signal processing problem of forming a beam pattern of an array of sensors. In doing so, beams are directed to the desired direction (beam-pointing) and the nulls are directed to interference direction (null-steering). In this paper, the performance of beamforming using the fully complex-valued RBF network (FC-RBF) with the fully complex-valued activation function is compared...
This paper presents an adaptive neural flight control design for helicopters performing nonlinear maneuver. The control strategy uses a neural controller aiding an existing conventional controller. The neural controller uses a real-time learning dynamic radial basis function network, which uses Lyapunov based on-line update rule integrated with the neuron growth criterion. The real-time learning dynamic...
In this paper, a fully complex radial basis function (FC-RBF) network and a gradient descent learning algorithm are presented. Many complex-valued RBF learning algorithms have been presented in the literature using a split-complex network which uses a real activation function in the hidden layer, i.e., the activation function in these network maps Cn rarr R. Hence these algorithms do not consider...
In a fully complex-valued feed-forward network, the convergence of the complex-valued back-propagation learning algorithm depends on the choice of the activation function, minimization criterion, initial weights and the learning rate. The minimization criteria used in the existing learning algorithms do not approximate the phase well in complex-valued function approximation problems. This aspect is...
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