Track topics on Twitter Track topics that are important to you
One of the major obstacles in using radial basis function (RBF) neural networks is the convergence toward local minima instead of the global minima. For this reason, an adaptive gradient multiobjective particle swarm optimization (AGMOPSO) algorithm is designed to optimize both the structure and parameters of RBF neural networks in this paper. First, the AGMOPSO algorithm, based on a multiobjective gradient method and a self-adaptive flight parameters mechanism, is developed to improve the computation performance. Second, the AGMOPSO-based self-organizing RBF neural network (AGMOPSO-SORBF) can optimize the parameters (centers, widths, and weights), as well as determine the network size. The goal of AGMOPSO-SORBF is to find a tradeoff between the accuracy and the complexity of RBF neural networks. Third, the convergence analysis of AGMOPSO-SORBF is detailed to ensure the prerequisite of any successful applications. Finally, the merits of our proposed approach are verified on multiple numerical examples. The results indicate that the proposed AGMOPSO-SORBF achieves much better generalization capability and compact network structure than some other existing methods.
This article was published in the following journal.
Name: IEEE transactions on cybernetics
A method that uses an adaptive learning rate is presented for training neural networks. Unlike most conventional updating methods in which the learning rate gradually decreases during training, the pr...
Recently, an adaptive exponential trigonometric functional link neural network (AETFLN) architecture has been introduced to enhance the nonlinear processing capability of the trigonometric functional ...
The robust control synthesis of continuous-time nonlinear systems with uncertain term is investigated via event-triggering mechanism and adaptive critic learning technique. We mainly focus on combinin...
This paper addresses the problem of robotic manipulators with unknown deadzone. In order to tackle the uncertainty and the unknown deadzone effect, we introduce adaptive neural network (NN) control fo...
Working memory requires information about external stimuli to be represented in the brain even after those stimuli go away. This information is encoded in the activities of neurons, and neural activit...
This study is designed to evaluate the feasibility of The Adaptive Neural Systems Neural-Enabled Prosthetic Hand (ANS-NEPH) system.
The purpose of the study is to determine the validity of the prediction model in reducing the rate of CPAP titration failure and in achieving a shorter time to optimal pressure
Healthy ageing and pathological ageing in the context of a neurodegenerative disease are both associated with changes in brain network integrity. Episodic memory is especially affected in ...
Identifying the correct arrhythmia at the time of a clinic event including cardiac arrest is of high priority to patients, healthcare organizations, and to public health. Recent developmen...
Continuous deep brain stimulation (cDBS) is an established therapy for the major motor signs in Parkinson's disease, however some patients find that it does not adequately treat their free...
Separation of particles according to density by employing a gradient of varying densities. At equilibrium each particle settles in the gradient at a point equal to its density. (McGraw-Hill Dictionary of Scientific and Technical Terms, 4th ed)
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.
A technique used to separate particles according to their densities in a continuous density gradient. The sample is usually mixed with a solution of known gradient materials and subjected to centrifugation. Each particle sediments to the position at which the gradient density is equal to its own. The range of the density gradient is usually greater than that of the sample particles. It is used in purifying biological materials such as proteins, nucleic acids, organelles, and cell types.
An early embryonic developmental process of CHORDATES that is characterized by morphogenic movements of ECTODERM resulting in the formation of the NEURAL PLATE; the NEURAL CREST; and the NEURAL TUBE. Improper closure of the NEURAL GROOVE results in congenital NEURAL TUBE DEFECTS.
The two longitudinal ridges along the PRIMITIVE STREAK appearing near the end of GASTRULATION during development of nervous system (NEURULATION). The ridges are formed by folding of NEURAL PLATE. Between the ridges is a neural groove which deepens as the fold become elevated. When the folds meet at midline, the groove becomes a closed tube, the NEURAL TUBE.