scholarly journals Landscape Planning and Image Analysis Based on Multipopulation Coevolution Particle Swarm Radial Basis Function Neural Network Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yang Wang
Keyword(s):  
Neural Network ◽  
Urban Planning ◽  
Radial Basis Function ◽  
Basis Function ◽  
Landscape Planning ◽  
Particle Swarm ◽  
Planning And Design ◽  
Network Algorithm ◽  
Radial Basis ◽  
Neural Network Algorithm

Urban landscape planning and design is not only closely related to people’s living environment, but also has an important impact on urban planning and development. However, there are some problems in landscape planning and design, such as excellent cases, low reuse rate of data, discrepancy between design scheme and actual situation, and serious shortage of relevant professionals. The artificial neural network can give corresponding ways to improve and solve these problems. Therefore, this paper proposes a research on garden planning and design based on multipopulation coevolution particle swarm radial basis function neural network algorithm. Based on multipopulation coevolution particle swarm radial basis function neural network algorithm, the error between the predicted evaluation value and the actual evaluation value in the simulation experiment is less than 5%, which shows good accuracy and generalization ability in performance. And in the plant configuration simulation experiment, it can effectively evaluate the urban planning and design and put forward the corresponding adjustment scheme according to the analysis results, which is more in line with the actual needs of urban planning.

Radial basis function neural network algorithm for semi-active control of base-isolated structures

2017 ◽  
Vol 24 (10) ◽  
pp. e1984 ◽  
Author(s):  
Agrahara Krishnamoorthy ◽  
Shubha Bhat ◽  
Dattatreya Bhasari
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Active Control ◽  
Basis Function ◽  
Network Algorithm ◽  
Radial Basis ◽  
Neural Network Algorithm ◽  
Isolated Structures

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.

An Integrated Hybrid Algorithm Based on Nature Inspired Evolutionary for Radial Basis Function Neural Network Learning

2016 ◽  
Vol 25 (02) ◽  
pp. 1650004 ◽  
Author(s):  
Zhen-Yao Chen ◽  
R. J. Kuo ◽  
Tung-Lai Hu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Radial Basis Function ◽  
Basis Function ◽  
Hybrid Algorithm ◽  
Particle Swarm ◽  
Learning Performance ◽  
Benchmark Problems ◽  
Neural Network Learning ◽  
Radial Basis

This paper intends to propose an integrated hybrid algorithm for training radial basis function neural network (RBFNN) learning. The proposed integrated of particle swarm and genetic algorithm based optimization (IPGO) algorithm is composed of two approaches based on particle swarm optimization (PSO) and genetic algorithm (GA) for gathering both their virtues to improve the learning performance of RBFNN. The diversity of individuals results in higher chance to search in the direction of global optimal instead of being confined to local optimal particularly in problem with higher complexity. The IPGO algorithm with PSO-based and GA-based approaches has shown promising results in some benchmark problems with three continuous test functions. After proposing the algorithm for these problems with result providing its outperforming performance, this paper supplements a practical application case for the papaya milk sales forecasting to expound the superiority of the IPGO algorithm. In addition, model evaluation results of the case have showed that the IPGO algorithm outperforms other algorithms and auto-regressive moving average (ARMA) models in terms of forecasting accuracy and execution time.

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