A density adjustment based particle swarm optimization learning algorithm for neural network design

2011 ◽  
Author(s):  
Xinghua Wu
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Network Design ◽  
Learning Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization

scholarly journals Unstable System Control Using an Improved Particle Swarm Optimization-Based Neural Network Controller

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1302 ◽  
Author(s):  
Cheng-Jian Lin ◽  
Xin-You Lin ◽  
Jyun-Yu Jhang
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Learning Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Pendulum System ◽  
Improved Particle Swarm Optimization ◽  
Inverted Pendulum System ◽  
Neural Network Controller ◽  
Network Controller

In this study, an improved particle swarm optimization (IPSO)-based neural network controller (NNC) is proposed for solving a real unstable control problem. The proposed IPSO automatically determines an NNC structure by a hierarchical approach and optimizes the parameters of the NNC by chaos particle swarm optimization. The proposed NNC based on an IPSO learning algorithm is used for controlling a practical planetary train-type inverted pendulum system. Experimental results show that the robustness and effectiveness of the proposed NNC based on IPSO are superior to those of other methods.

A neuro-particle swarm optimization logistic model fitting algorithm for software reliability analysis

2019 ◽  
Vol 233 (6) ◽  
pp. 958-971 ◽  
Author(s):  
Pooja Rani ◽  
GS Mahapatra
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Software Reliability ◽  
Particle Swarm Optimization Algorithm ◽  
Learning Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Text Model

This article develops a particle swarm optimization algorithm based on a feed-forward neural network architecture to fit software reliability growth models. We employ adaptive inertia weight within the proposed particle swarm optimization in consideration of learning algorithm. The dynamic adaptive nature of proposed prior best particle swarm optimization prevents the algorithm from becoming trapped in local optima. These neuro-prior best particle swarm optimization algorithms were applied to a popular flexible logistic growth curve as the [Formula: see text] model based on the weights derived by the artificial neural network learning algorithm. We propose the prior best particle swarm optimization algorithm to train the network for application to three different software failure data sets. The new search strategy improves the rate of convergence because it retains information on the prior particle, thereby enabling better predictions. The results are verified through testing approaching of constant, modified, and linear inertia weight. We assess the fitness of each particle according to the normalized root mean squared error which updates the best particle and velocity to accelerate convergence to an optimal solution. Experimental results demonstrate that the proposed [Formula: see text] model based prior best Particle Swarm Optimization based on Neural Network (pPSONN) improves predictive quality over the [Formula: see text], [Formula: see text], and existing model.

Training oscillatory neural networks using natural gradient particle swarm optimization

Robotica ◽  
2014 ◽  
Vol 33 (7) ◽  
pp. 1551-1567 ◽  
Author(s):  
Hamed Shahbazi ◽  
Kamal Jamshidi ◽  
Amir Hasan Monadjemi ◽  
Hafez Eslami Manoochehri
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Transfer Functions ◽  
Learning Algorithm ◽  
Particle Swarm ◽  
Humanoid Robots ◽  
Unknown Parameters ◽  
Swarm Optimization ◽  
Oscillatory Neural Network

SUMMARYIn this paper, a new design of neural networks is introduced, which is able to generate oscillatory patterns in its output. The oscillatory neural network is used in a biped robot to enable it to learn to walk. The fundamental building block of the neural network proposed in this paper is O-neurons, which can generate oscillations in its transfer functions. O-neurons are connected and coupled with each other in order to shape a network, and their unknown parameters are found by a particle swarm optimization method. The main contribution of this paper is the learning algorithm that can combine natural policy gradient with particle swarm optimization methods. The oscillatory neural network has six outputs that determine set points for proportional-integral-derivative controllers in 6-DOF humanoid robots. Our experiment on the simulated humanoid robot presents smooth and flexible walking.

scholarly journals Improved Chaotic Quantum-Behaved Particle Swarm Optimization Algorithm for Fuzzy Neural Network and Its Application

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yuexi Peng ◽  
Kejun Lei ◽  
Xi Yang ◽  
Jinzhang Peng
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Fuzzy Neural Network ◽  
Particle Swarm Optimization Algorithm ◽  
Learning Algorithm ◽  
Particle Swarm ◽  
Hunan Province ◽  
Swarm Optimization ◽  
Fuzzy Neural

Traditional fuzzy neural network has certain drawbacks such as long computation time, slow convergence rate, and premature convergence. To overcome these disadvantages, an improved quantum-behaved particle swarm optimization algorithm is proposed as the learning algorithm. In this algorithm, a new chaotic search is introduced, and benchmark function experiments prove it outperforms the other five existing algorithms. Finally, the proposed algorithm is presented as the learning algorithm for Takagi–Sugeno fuzzy neural network to form a new neural network, and it is utilized in the water quality evaluation of Dongjiang Lake of Hunan province. Simulation results demonstrated the effectiveness of the new neural network.

scholarly journals Prediction Model of Corrosion Current Density Induced by Stray Current Based on QPSO-Driven Neural Network

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Chengtao Wang ◽  
Wei Li ◽  
Gaifang Xin ◽  
Yuqiao Wang ◽  
Shaoyi Xu
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Current Density ◽  
Corrosion Current Density ◽  
Learning Algorithm ◽  
Particle Swarm ◽  
Electrochemical Corrosion ◽  
Corrosion Current ◽  
Stray Current ◽  
Swarm Optimization

The buried pipelines and metallic structures in subway systems are subjected to electrochemical corrosion under the stray current interference. The corrosion current density determines the degree and the speed of stray current corrosion. A method combining electrochemical experiment with the machine learning algorithm was utilized in this research to study the corrosion current density under the coupling action of stray current and chloride ion. In this study, a quantum particle swarm optimization-neural network (QPSO-NN) model was built up to predict the corrosion current density in the process of stray current corrosion. The QPSO algorithm was employed to optimize the updating process of weights and biases in the artificial neural network (ANN). The results show that the accuracy of the proposed QPSO-NN model is better than the model based on backpropagation neural network (BPNN) and particle swarm optimization-neural network (PSO-NN). The accuracy distribution of the QPSO-NN model is more stable than that of the BPNN model and the PSO-NN model. The presented model can be used for the prediction of corrosion current density and provides the possibility to monitor the stray current corrosion in subway system through an intelligent learning algorithm.

The Application of BP Neural Network Learning Algorithm Based on the Particle Swarm Optimization

2013 ◽  
Vol 706-708 ◽  
pp. 2057-2062
Author(s):  
Zhi Hong Sun ◽  
Jun Wang ◽  
Bao Ji Xu
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Real Estate ◽  
Bp Neural Network ◽  
Learning Algorithm ◽  
Particle Swarm ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Neural Network Learning ◽  
Network Learning

The development of real estate has been affected by various social factors, including economic factors. BP neural network can more accurately forecast the trend of real estate industry according to economic development indicators. But BP neural network is slow convergence in the training process, and easily falls into local optimum. The BP neural network learning algorithm based on the particle swarm optimization (PSO) optimizes the weights and thresholds of the network by PSO algorithm, then to train BP neural network. The experimental results show that the performance of this new algorithm is better than BP neural network, but also has good convergence.

Analysis of Feature Reduction Techniques for Online News Popularity Prediction

2018 ◽  
Vol 4 (10) ◽  
pp. 6
Author(s):  
Shivangi Bhargava ◽  
Dr. Shivnath Ghosh
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Online News ◽  
Feature Reduction ◽  
Support Vector ◽  
Swarm Optimization

News popularity is the maximum growth of attention given for particular news article. The popularity of online news depends on various factors such as the number of social media, the number of visitor comments, the number of Likes, etc. It is therefore necessary to build an automatic decision support system to predict the popularity of the news as it will help in business intelligence too. The work presented in this study aims to find the best model to predict the popularity of online news using machine learning methods. In this work, the result analysis is performed by applying Co-relation algorithm, particle swarm optimization and principal component analysis. For performance evaluation support vector machine, naïve bayes, k-nearest neighbor and neural network classifiers are used to classify the popular and unpopular data. From the experimental results, it is observed that support vector machine and naïve bayes outperforms better with co-relation algorithm as well as k-NN and neural network outperforms better with particle swarm optimization.

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