Prediction of CO2Solubility in Polymers by Radial Basis Function Artificial Neural Network Based on Chaotic Self-adaptive Particle Swarm Optimization and Fuzzy Clustering Method

2013 ◽  
Vol 31 (12) ◽  
pp. 1564-1572 ◽  
Author(s):  
Yan Wu ◽  
Bingxiang Liu ◽  
Mengshan Li ◽  
Kezong Tang ◽  
Yubo Wu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Particle Swarm Optimization ◽  
Radial Basis Function ◽  
Fuzzy Clustering ◽  
Basis Function ◽  
Clustering Method ◽  
Swarm Optimization ◽  
Adaptive Particle Swarm Optimization ◽  
Fuzzy Clustering Method

scholarly journals Design of Polynomial Fuzzy Radial Basis Function Neural Networks Based on Nonsymmetric Fuzzy Clustering and Parallel Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-10
Author(s):  
Wei Huang ◽  
Jinsong Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Radial Basis Function ◽  
Fuzzy Clustering ◽  
Basis Function ◽  
Parallel Optimization ◽  
Clustering Method ◽  
Fair Competition ◽  
Fuzzy Clustering Method ◽  
Radial Basis

We first propose a Parallel Space Search Algorithm (PSSA) and then introduce a design of Polynomial Fuzzy Radial Basis Function Neural Networks (PFRBFNN) based on Nonsymmetric Fuzzy Clustering Method (NSFCM) and PSSA. The PSSA is a parallel optimization algorithm realized by using Hierarchical Fair Competition strategy. NSFCM is essentially an improved fuzzy clustering method, and the good performance in the design of “conventional” Radial Basis Function Neural Networks (RBFNN) has been proven. In the design of PFRBFNN, NSFCM is used to design the premise part of PFRBFNN, while the consequence part is realized by means of weighted least square (WLS) method. Furthermore, HFC-PSSA is exploited here to optimize the proposed neural network. Experimental results demonstrate that the proposed neural network leads to better performance in comparison to some existing neurofuzzy models encountered in the literature.

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.

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