PARTICLE SWARM LEARNING ALGORITHM BASED ON ADJUSTMENT OF PARAMETER AND ITS APPLICATIONS ASSESSMENT OF AGRICULTURAL PROJECTS

Particle swarm optimization (PSO) is introduced to implement a new constructive learning algorithm for training generalized cellular neural networks (GCNNs) for the identification of spatio-temporal evolutionary (STE) systems. The basic idea of the new PSO-based learning algorithm is to successively approximate the desired signal by progressively pursuing relevant orthogonal projections. This new algorithm will thus be referred to as the orthogonal projection pursuit (OPP) algorithm, which is in mechanism similar to the conventional projection pursuit approach. A novel two-stage hybrid training scheme is proposed for constructing a parsimonious GCNN model. In the first stage, the orthogonal projection pursuit algorithm is applied to adaptively and successively augment the network, where adjustable parameters of the associated units are optimized using a particle swarm optimizer. The resultant network model produced at the first stage may be redundant. In the second stage, a forward orthogonal regression (FOR) algorithm, aided by mutual information estimation, is applied to refine and improve the initially trained network. The effectiveness and performance of the proposed method is validated by applying the new modeling framework to a spatio-temporal evolutionary system identification problem.

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On the Identification of Coupled Pitch and Heave Motions Using Opposition-Based Particle Swarm Optimization

Mathematical Problems in Engineering ◽

10.1155/2014/784049 ◽

2014 ◽

Vol 2014 ◽

pp. 1-10 ◽

Cited By ~ 8

Author(s):

Yuntao Dai ◽

Liqiang Liu ◽

Shanshan Feng

Keyword(s):

Particle Swarm Optimization ◽

Parameter Identification ◽

Learning Algorithm ◽

Particle Swarm ◽

Identification System ◽

Identification Algorithm ◽

Swarm Optimization ◽

Disturbing Force ◽

Hydrodynamic Parameters ◽

Opposition Based Learning

A mathematical model must be established to study the motions of ships in order to control them effectively. An assessment of the model depends on the accuracy of hydrodynamic parameters. An algorithm for the parameter identification of the coupled pitch and heave motions in ships is, thus, put forward in this paper. The algorithm proposed is based on particle swarm optimization (PSO) and the opposition-based learning theory known as opposition-based particle swarm optimization (OPSO). A definition of the opposition-based learning algorithm is given first of all, with ideas on how to improve this algorithm and its process being presented next. Secondly, the design of the parameter identification algorithm is put forward, modeling the disturbing force and disturbing moment of the identification system and the output parameters of the identification system. Then, the problem involving the hydrodynamic parameters of motions is identified and the coupled pitch and heave motions of a ship described as an optimization problem with constraints. Finally, the numerical simulations of different sea conditions with unknown parameters are carried out using the PSO and OPSO algorithms. The simulation results show that the OPSO algorithm is relatively stable in terms of the hydrodynamic parameters identification of the coupled pitch and heave motions.

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Improved SpikeProp for Using Particle Swarm Optimization

Mathematical Problems in Engineering ◽

10.1155/2013/257085 ◽

2013 ◽

Vol 2013 ◽

pp. 1-13 ◽

Cited By ~ 6

Author(s):

Falah Y. H. Ahmed ◽

Siti Mariyam Shamsuddin ◽

Siti Zaiton Mohd Hashim

Keyword(s):

Particle Swarm Optimization ◽

Supervised Learning ◽

Learning Algorithm ◽

Particle Swarm ◽

Learning Rule ◽

Temporal Coding ◽

Spiking Neurons ◽

Learning Enhancement ◽

Swarm Optimization ◽

Spiking Networks

A spiking neurons network encodes information in the timing of individual spike times. A novel supervised learning rule for SpikeProp is derived to overcome the discontinuities introduced by the spiking thresholding. This algorithm is based on an error-backpropagation learning rule suited for supervised learning of spiking neurons that use exact spike time coding. The SpikeProp is able to demonstrate the spiking neurons that can perform complex nonlinear classification in fast temporal coding. This study proposes enhancements of SpikeProp learning algorithm for supervised training of spiking networks which can deal with complex patterns. The proposed methods include the SpikeProp particle swarm optimization (PSO) and angle driven dependency learning rate. These methods are presented to SpikeProp network for multilayer learning enhancement and weights optimization. Input and output patterns are encoded as spike trains of precisely timed spikes, and the network learns to transform the input trains into target output trains. With these enhancements, our proposed methods outperformed other conventional neural network architectures.

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Development of Neural-Net-Based Decision Support System for Mattress Patterns Using Particle Swarm Optimization

International Journal of Automation Technology ◽

10.20965/ijat.2010.p0178 ◽

2010 ◽

Vol 4 (2) ◽

pp. 178-183 ◽

Cited By ~ 2

Author(s):

Mitsue Kato ◽

◽

Toru Yamamoto ◽

Keyword(s):

Particle Swarm Optimization ◽

Decision Support ◽

Decision Support System ◽

Support System ◽

Optimization Problems ◽

Learning Algorithm ◽

Particle Swarm ◽

Neural Net ◽

Swarm Optimization ◽

Neural Network Learning

The particle swarm optimization (PSO) concept simulating a simplified social milieu, is optimization useful for solving nonconvex continuous optimization problems. We discuss a new learning algorithm for simultaneously adjusting connection weights in neural networks and user-specified parameters included in units. Based on our algorithm, neural network learning, e.g., learning cost or adaptability, can be improved, as demonstrated in mattress decision support system.

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HYBRID RECURSIVE PARTICLE SWARM OPTIMIZATION LEARNING ALGORITHM IN THE DESIGN OF RADIAL BASIS FUNCTION NETWORKS

Journal of Marine Science and Technology ◽

10.51400/2709-6998.2030 ◽

2007 ◽

Vol 15 (1) ◽

Cited By ~ 1

Author(s):

Ching-Yi Chen ◽

Hsuan-Ming Feng ◽

Fun Ye

Keyword(s):

Particle Swarm Optimization ◽

Radial Basis Function ◽

Basis Function ◽

Learning Algorithm ◽

Particle Swarm ◽

Radial Basis Function Networks ◽

Swarm Optimization ◽

Radial Basis

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Learning Bayesian networks based on bi-velocity discrete particle swarm optimization with mutation operator

Open Mathematics ◽

10.1515/math-2018-0086 ◽

2018 ◽

Vol 16 (1) ◽

pp. 1022-1036

Author(s):

Jingyun Wang ◽

Sanyang Liu

Keyword(s):

Particle Swarm Optimization ◽

Bayesian Networks ◽

Structure Learning ◽

Learning Algorithm ◽

Particle Swarm ◽

Mutation Operator ◽

Discrete Particle Swarm Optimization ◽

Swarm Optimization ◽

Discrete Particle ◽

Mutation Strategy

AbstractThe problem of structures learning in Bayesian networks is to discover a directed acyclic graph that in some sense is the best representation of the given database. Score-based learning algorithm is one of the important structure learning methods used to construct the Bayesian networks. These algorithms are implemented by using some heuristic search strategies to maximize the score of each candidate Bayesian network. In this paper, a bi-velocity discrete particle swarm optimization with mutation operator algorithm is proposed to learn Bayesian networks. The mutation strategy in proposed algorithm can efficiently prevent premature convergence and enhance the exploration capability of the population. We test the proposed algorithm on databases sampled from three well-known benchmark networks, and compare with other algorithms. The experimental results demonstrate the superiority of the proposed algorithm in learning Bayesian networks.

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Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms

Computational Intelligence and Neuroscience ◽

10.1155/2015/369298 ◽

2015 ◽

Vol 2015 ◽

pp. 1-20 ◽

Cited By ~ 63

Author(s):

Beatriz A. Garro ◽

Roberto A. Vázquez

Keyword(s):

Particle Swarm Optimization ◽

Transfer Functions ◽

Learning Algorithm ◽

Optimization Algorithms ◽

Back Propagation ◽

Particle Swarm ◽

Classification Error ◽

Classification Problems ◽

Swarm Optimization ◽

Artificial Neural

Artificial Neural Network (ANN) design is a complex task because its performance depends on the architecture, the selected transfer function, and the learning algorithm used to train the set of synaptic weights. In this paper we present a methodology that automatically designs an ANN using particle swarm optimization algorithms such as Basic Particle Swarm Optimization (PSO), Second Generation of Particle Swarm Optimization (SGPSO), and a New Model of PSO called NMPSO. The aim of these algorithms is to evolve, at the same time, the three principal components of an ANN: the set of synaptic weights, the connections or architecture, and the transfer functions for each neuron. Eight different fitness functions were proposed to evaluate the fitness of each solution and find the best design. These functions are based on the mean square error (MSE) and the classification error (CER) and implement a strategy to avoid overtraining and to reduce the number of connections in the ANN. In addition, the ANN designed with the proposed methodology is compared with those designed manually using the well-known Back-Propagation and Levenberg-Marquardt Learning Algorithms. Finally, the accuracy of the method is tested with different nonlinear pattern classification problems.

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