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DIRECT is known for balancing the exploration and exploitation of a search space. This paper seeks to explore the improvement of diversity among solutions through the use of qualitative indicators in multi-objective DIRECT framework. Three different indicators - Hypervolume (HV), Epsilon (EPS), R2 indicators are used in this study. The three variants of indicators are tested on the Black-box Multi-objective...
In this paper, we propose an interval type-2 fuzzy inference system using Extended Kalman Filter based learning algorithm. It is referred to as IT2FIS-EKF. This algorithm realizes the Takagi-Sugeno-Kang inference mechanism in a five layered architecture. It starts with no rules and evolves the structure automatically. The sequential learning algorithm regulates the learning process by selecting appropriate...
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...
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...
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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