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Robot formation control based on Internet of things technology platform
2020
IEEE Access
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Finally, the simulation of robot formation motion is established by MATLAB software, which verifies the feasibility of particle swarm optimization deep learning neural network algorithm under the Internet ...
INDEX TERMS Robot, formation cooperative control, Internet of things, particle swarm optimization, deep learning. ...
neural network algorithm under particle swarm optimization more stable in application. ...
doi:10.1109/access.2020.2992701
fatcat:7wobxm66xnbx5kk2mcqct46gby
A Hybrid of Cooperative Particle Swarm Optimization and Cultural Algorithm for Neural Fuzzy Networks and Its Prediction Applications
2009
IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)
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It is thus called cultural cooperative particle swarm optimization (CCPSO). ...
This new evolutionary learning algorithm is based on a hybrid of cooperative particle swarm optimization and cultural algorithm. ...
CONCLUSION This study proposes an efficient cultural cooperative particle swarm optimization learning method for the functionallink-based neural fuzzy network in predictive applications. ...
doi:10.1109/tsmcc.2008.2002333
fatcat:5rhoha3qiff6bh5iww5pp6hsmu
Fault Diagnosis of High-Power Tractor Engine Based on Competitive Multiswarm Cooperative Particle Swarm Optimizer Algorithm
2020
Shock and Vibration
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was reconstructed, and a competitive multiswarm cooperative particle swarm optimizer algorithm (COM-MCPSO) was used to optimize its structure and weights. ...
In this paper, due to high-power tractor diesel engine fault complexity, fault correlation, and multifault concurrency, a multigroup coevolution particle swarm optimization BP neural network for diesel ...
Multiswarm Cooperative Particle Swarm Optimizer Optimize BP Neural Network. In nature, the evolution of populations is mostly caused by the interaction of different populations. ...
doi:10.1155/2020/8829257
fatcat:xghd6osrqzg3zeo3d7fr47nl2a
A Survey on Automatic Design Methods for Swarm Robotics Systems
2021
Carpathian Journal of Electronic and Computer Engineering
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In general, they follow two-approach evolutionary algorithms like practical swarm optimization and reinforcement learning. ...
, and explaining the methods and advantages of using deep learning to reinforcement learning. ...
Particles swarm optimization PSO : Particles swarm optimization is one of the well-regarded algorithms in the literature of optimization and has been widely used in various science and industry fields. ...
doi:10.2478/cjece-2021-0006
fatcat:ri6uvht5ybhtfbmifo463zkkka
Research on a New Hybrid Optimization Algorithm based on QPSO and FNN
2016
International Journal of Smart Home
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Fuzzy neural network(FNN) is a neural network based on combining the advantages of the fuzzy theory and neural network. ...
Particle swarm optimization(PSO) algorithm is a population-based search algorithm by simulating the social behavior of birds within a flock. ...
The program for the initialization, training, and simulation of the proposed algorithm in this article was written with the tool-box of MATLAB 2012b produced by the Math-Works, Inc. ...
doi:10.14257/ijsh.2016.10.6.18
fatcat:b7jbpxj2tnewri2ouxer5ofzbq
Direct Zero-Norm Minimization for Neural Network Pruning and Training
[chapter]
2012
Communications in Computer and Information Science
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The method employs a cooperative scheme using two swarms of particles and its purpose is to minimize an aggregate function corresponding to the total risk functional. ...
Designing a feed-forward neural network with optimal topology in terms of complexity (hidden layer nodes and connections between nodes) and training performance has been a matter of considerable concern ...
For example, in system identification using neural networks, finding the optimal network architecture is not straightforward [1] . ...
doi:10.1007/978-3-642-32909-8_30
fatcat:pc64552wn5fnpjojsiqletiqvi
AN EFFECTIVE ARCHETYPE DESIGN OF HEART DISEASE ANTICIPATION USING OPTIMIZATION TECHNIQUES
2019
EPRA international journal of research & development
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KEYWORDS: Data mining, Classification, Particle Swarm Optimization, Cardiovascular Disease(CVD). ...
In this review, we focus the novel and unique aspects of cardiovascular disease health and the methodologies used to predict the CVD. ...
Jairam P.Kelwade
et al[13]
2016
Prediction of heart
abnormalities using Particle
Swarm Optimization in
Radial Basis Function Neural
Network
Hybridized Particle
Swarm Optimization
and Radial ...
doi:10.36713/epra3747
fatcat:2eronako5zh55nhg6b4vlix6jm
Symbiotic Particle Swarm Optimization for Neural Fuzzy Controllers
2014
International Journal of Machine Learning and Computing
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Index Terms-Water bath temperature system, neural fuzzy networks, symbiotic evolution, particle swarm optimization. ...
The proposed SPSO adopts a multiple swarm scheme that uses each particle represents a single fuzzy rule and each particle in each swarm evolves separately to avoid falling in a local optimal solution. ...
The major novelty of the proposed SPSO learning algorithm uses a multiple swarm scheme to allow that each individual in each swarm evolves separately using a specific particle swarm optimization for constructing ...
doi:10.7763/ijmlc.2014.v4.450
fatcat:odpq3xduenbw7aqx5i3hrgnoii
A Novel Quantum-behaved Particle Swarm Optimization Algorithm and Its Application to Parameter Optimization of Fuzzy Neural Networks
2012
Advances in Information Sciences and Service Sciences
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Then the novel algorithm was applied to parameter optimization of fuzzy neural networks. The introduction of cooperative learning strategy is the originality in the proposed method. ...
A novel Quantum-behaved Particle Swarm Optimization Algorithm with comprehensive learning and cooperative learning approach (CCQPSO) was introduced to improve the global convergence property of QPSO. ...
Fuzzy Neural Networks (FNN) has the advantage of fuzzy system and neural networks [7] , which includes the exact fitting and learning ability of neural networks and the powerful knowledge representation ...
doi:10.4156/aiss.vol4.issue22.46
fatcat:it3d2jbzsrdy3lq3jwz7a33mgy
Overlapping swarm intelligence for training artificial neural networks
2011
2011 IEEE Symposium on Swarm Intelligence
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A novel overlapping swarm intelligence algorithm is introduced to train the weights of an artificial neural network. ...
On the other hand, training algorithms based on evolutionary computation have been used to train multilayer feed-forward networks in an attempt to overcome the limitations of gradient based algorithms ...
In their work [6] , they explain that certain deceptive functions could stagnate the evolution of particles when cooperative learning is used in PSO. ...
doi:10.1109/sis.2011.5952566
dblp:conf/swis/PillaiS11
fatcat:jrsrec3dgfe3zfm574ze3noyge
An Evolutionary Functional Link Neural Fuzzy Model for Financial Time Series Forecasting
2011
International Journal of Applied Evolutionary Computation
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A single multiplicative neuron with cooperative random learning particle swarm optimization is applied (Zhao & Yang, 2009 ) to predict Mackey glass time series. ...
A local linear wavelet neural network (LLWNN) (Chen, Yang, & Dong, 2006 ) is used to predict Box-Jenkins and Mackey glass time series where a hybrid training algorithm of particle swarm optimization and ...
doi:10.4018/jaec.2011070104
fatcat:y6ykaolnrzelxcywasw7axjn4a
Motion model identification of rescue robot based on optimized Jordan neural network
2017
IOP Conference Series: Earth and Environment
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The improved Jordan network is optimized by chaos particle swarm optimization algorithm. The optimized neural network is applied to identify the dynamic model of the underwater rescue robot. ...
The network can be used to remember the state of the hidden layer and increase the feedback of the output node. ...
In this paper, the Jordan neural network is improved and a hybrid neural network is established. The chaotic particle swarm optimization algorithm is used to optimize the network structure. ...
doi:10.1088/1755-1315/69/1/012189
fatcat:ent5rt33lnfjnjp2lv72mhxjje
A Multi-population Cooperative Particle Swarm Optimizer for Neural Network Training
[chapter]
2006
Lecture Notes in Computer Science
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This paper presents a new learning algorithm, Multi-Population Cooperative Particle Swarm Optimizer (MCPSO), for neural network training. ...
The performance of MCPSO used for neural network training is compared to that of Back Propagation (BP), genetic algorithm (GA) and standard PSO (SPSO), demonstrating its effectiveness and efficiency. ...
Recently, a swarm intelligence method, particle swarm optimization (PSO), has been applied to many problems, including neural network design [3] . ...
doi:10.1007/11759966_85
fatcat:lo557v237zflrcqkjploln5x7e
A robot behavior-learning experiment using Particle Swarm Optimization for training a neural-based animat
2008
2008 10th International Conference on Control, Automation, Robotics and Vision
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Keywordsanimat, behavior-learning, genetic algorithms, particle swarm optimization, recurrent neural network. I. ...
We investigate the use of Particle Swarm Optimization (PSO), and compare with Genetic Algorithms (GA), for a particular robot behavior-learning task: the training of an animat behavior totally determined ...
COMPARING PSO-BASED AND GA-BASED TRAINING We can now compare, on the exact same task described in §III, unsupervised learning with Particle Swarm Optimization (PSO), and using Genetic Algorithms (GA). ...
doi:10.1109/icarcv.2008.4795790
dblp:conf/icarcv/Moutarde08
fatcat:dxvrycvcobaxtmezpf6c6uozbu
Adaptive Control based Particle Swarm Optimization and Chebyshev Neural Network for Chaotic Systems
2014
Journal of Computers
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Index Terms-adaptive control, particle swarm optimization, Chebyshev neural networks, chaotic systems Zhen Hong received both B. ...
The particle swarm optimization (PSO) algorithm is firstly proposed to search for the weights of the Chebyshev neural networks (CNNs), and then an adaptive controller for the chaotic systems is designed ...
We also determine the fitness function in particle swarm optimization according to the Eq. (7) . ...
doi:10.4304/jcp.9.6.1385-1390
fatcat:32lvm3id2zho7fprqundb7swna
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