Starch foam material performance prediction based on a radial basis function artificial neural network trained by bare-bones particle swarm optimization with an adaptive disturbance factor

2016 ◽  
Vol 133 (47) ◽  
Author(s):  
Ruting Xia ◽  
Xingyuan Huang ◽  
Mengshan Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Particle Swarm Optimization ◽  
Radial Basis Function ◽  
Performance Prediction ◽  
Basis Function ◽  
Foam Material ◽  
Swarm Optimization ◽  
Disturbance Factor ◽  
Bare Bones

PREDICTION OF PARKINSON'S DISEASE TREMOR ONSET USING A RADIAL BASIS FUNCTION NEURAL NETWORK BASED ON PARTICLE SWARM OPTIMIZATION

2010 ◽  
Vol 20 (02) ◽  
pp. 109-116 ◽  
Author(s):  
DEFENG WU ◽  
KEVIN WARWICK ◽  
ZI MA ◽  
MARK N. GASSON ◽  
JONATHAN G. BURGESS ◽  
...  
Keyword(s):  
Neural Network ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Particle Swarm Optimization ◽  
Radial Basis Function ◽  
Basis Function ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Radial Basis ◽  
Deep Brain

Deep Brain Stimulation (DBS) has been successfully used throughout the world for the treatment of Parkinson's disease symptoms. To control abnormal spontaneous electrical activity in target brain areas DBS utilizes a continuous stimulation signal. This continuous power draw means that its implanted battery power source needs to be replaced every 18–24 months. To prolong the life span of the battery, a technique to accurately recognize and predict the onset of the Parkinson's disease tremors in human subjects and thus implement an on-demand stimulator is discussed here. The approach is to use a radial basis function neural network (RBFNN) based on particle swarm optimization (PSO) and principal component analysis (PCA) with Local Field Potential (LFP) data recorded via the stimulation electrodes to predict activity related to tremor onset. To test this approach, LFPs from the subthalamic nucleus (STN) obtained through deep brain electrodes implanted in a Parkinson patient are used to train the network. To validate the network's performance, electromyographic (EMG) signals from the patient's forearm are recorded in parallel with the LFPs to accurately determine occurrences of tremor, and these are compared to the performance of the network. It has been found that detection accuracies of up to 89% are possible. Performance comparisons have also been made between a conventional RBFNN and an RBFNN based on PSO which show a marginal decrease in performance but with notable reduction in computational overhead.

Quantum-Behaved Particle Swarm Optimization Based Radial Basis Function Network for Classification of Clinical Datasets

2018 ◽  
Vol 9 (2) ◽  
pp. 32-52
Author(s):  
N. Leema ◽  
H. Khanna Nehemiah ◽  
A. Kannan
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Radial Basis Function ◽  
Basis Function ◽  
Clustering Algorithm ◽  
Radial Basis Function Network ◽  
Approximation Error ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Radial Basis

In this article, a classification framework that uses quantum-behaved particle swarm optimization neural network (QPSONN) classifiers for diagnosing a disease is discussed. The neural network used for classification is radial basis function neural network (RBFNN). For training the RBFNN K-means clustering algorithm and quantum-behaved particle swarm optimization (QPSO) algorithm has been used. The K-means clustering algorithm is used to find the optimal number of clusters which determines the number of neurons in the hidden layer. The cluster approximation error is used to find the optimal clusters. The weights between the hidden and the output layer is determined using QPSO algorithm based on the mean squared error (MSE). The performance of the developed classifier model has been tested with five clinical datasets, namely Pima Indian Diabetes, Hepatitis, Bupa Liver Disease, Wisconsin Breast Cancer and Cleveland Heart Disease were obtained from the University of California, Irvine (UCI) machine learning repository.

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