A Hybrid Blind Signal Separation Algorithm: Particle Swarm Optimization on Feed-Forward Neural Network

2006 ◽  
pp. 1078-1087
Author(s):  
Chan-Cheng Liu ◽  
Tsung-Ying Sun ◽  
Sheng-Ta Hsieh ◽  
Chun-Ling Lin ◽  
Kan-Yuan Lee
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Signal Separation ◽  
Blind Signal Separation ◽  
Separation Algorithm ◽  
Feed Forward Neural Network ◽  
Swarm Optimization ◽  
Feed Forward ◽  
Blind Signal

scholarly journals A Modified Particle Swarm Optimization with Neural Network via Euclidean Distance

2018 ◽  
Vol 6 (1) ◽  
pp. 4
Author(s):  
Asia L. Jabar ◽  
Tarik A. Rashid
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Euclidean Distance ◽  
Particle Swarm ◽  
Classification Problem ◽  
Feed Forward Neural Network ◽  
Distance Method ◽  
Swarm Optimization ◽  
Feed Forward ◽  
Hidden Layer

<p>In this paper, a new modified model of Feed Forward Neural Network with Particle Swarm Optimization via using Euclidean Distance method (FNNPSOED) is used to better handle a classification problem of the employee’s behavior. The Particle Swarm Optimization (PSO) as a natural inspired algorithm is used to support the Feed Forward Neural Network (FNN) with one hidden layer in obtaining the optimum weights and biases using different hidden layer neurons numbers. The key reason of using ED with PSO is to take the distance between each two-feature value then use this distance as a random number in the velocity equation for the velocity value in the PSO algorithm. The FNNPSOED is used to classify employees’ behavior using 29 unique features. The FNNPSOED is evaluated against the Feed Forward Neural Network with Particle Swarm Optimization (FNNPSO). The FNNPSOED produced satisfactory results.</p>

scholarly journals MODEL FEED FORWARD NEURAL NETWORK (FFNN) DENGAN ALGORITMA PARTICLE SWARM SEBAGAI OPTIMASI BOBOT (Studi Kasus : Harga Daging Sapi dari Bank Dunia Periode Januari 2007 – Desember 2018)

2019 ◽  
Vol 8 (1) ◽  
pp. 117-126
Author(s):  
Faisal Fikri Utama ◽  
Budi Warsito ◽  
Sugito Sugito
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Time Series Data ◽  
Parametric Method ◽  
Particle Swarm ◽  
Series Data ◽  
Feed Forward Neural Network ◽  
Swarm Optimization ◽  
Feed Forward ◽  
Particle Speed

Beef is one of the important food commodities to fulfill the nutritional adequacy of humans. The World Bank notes the beef prices that are exported worldwide every month. For this reason, those data becomes a predictable series for the next period. Feed Forward Neural Network is a non-parametric method that can be used to make predictions from time series data without having to be bound by classical assumptions. The initiated weight will be evaluated by an algorithm that can minimize errors. Particle Swarm Optimization (PSO) is an optimization algorithm based on particle speed to reach the destination. The FFNN model will be combined with PSO to get predictive results that are close to the target. The best architecture on FFNN is obtained with 2 units of input, 1 unit of bias, 3 hidden units, and 1 unit of output by producing MAPE training 11.7735% and MAPE testing 8.14%. According to Lewis (1982) in Moreno et. al (2013), the MAPE value below 10% is highly accurate forecasting. Keywords: Feed Forward Neural Network (FFNN), Particle Swarm Optimization (PSO), neurons, weights, predictions.

scholarly journals An Identification of Faults in Industries Using Hybrid Feed Forward Neural Network – Particle Swarm Optimization

2021 ◽  
Author(s):  
Anoopa Jose Chittilappilly ◽  
Kamalraj Subramaniam ◽  
Giby Jose ◽  
P. Manimegalai
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Fault Analysis ◽  
Feed Forward Neural Network ◽  
Swarm Optimization ◽  
Feed Forward ◽  
Local Means ◽  
Sensitivity Rate ◽  
Improved Performance

Abstract In order to enhance the quality governance in automotive firms the fault analysis and categorization is designed with optimal image renewals employing swarm intelligence scheme with improved precision classifier. Methodology: Preliminarily the accumulated information is preprocessed for eradicating the undesirable noise and renewal is achieved employing non – local means scheme, followed by which five characteristics like arithmetic mean, variance, standard deviation, skewness and auto correlation are mined. The mined characteristics are sent to the feed forward neural network (FFNN) classifier for recognizing faults in the computerized segments produced in the firms. In FFNN the particle swarm optimization (PSO) is employed to optimize the characteristics for effective fault identification in metal sheets. Results: The experimental analysis reveals that the designed FFNN – PSO scheme acquires improved performance with increased rate of accuracy of 92.86%, sensitivity rate of 95.24%, specificity rate of 90.48%, G – mean rate of 97.47% and precision rate of 90.90% evaluated against the prevailing classifiers.

Particle Swarm Optimization Neural Network for Flow Prediction in Vegetative Channel

2013 ◽  
Vol 22 (4) ◽  
pp. 487-501
Author(s):  
Anjaneya Jha ◽  
Bimlesh Kumar
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Hybrid Model ◽  
Particle Swarm ◽  
Model Performance ◽  
Analytical Form ◽  
Performance Criteria ◽  
Feed Forward Neural Network ◽  
Swarm Optimization ◽  
Flow Prediction

AbstractFlow prediction in a vegetated channel has been extensively studied in the past few decades. A number of equations that essentially differ from each other in derivation and form have been developed. Because the process is extremely complex, getting the deterministic or analytical form of the process phenomena is too difficult. Hybrid neural network model (combining particle swarm optimization with neural network) is particularly useful in modeling processes where an adequate knowledge of the physics is limited. This hybrid model is presented here as a complementary tool to model channel flow–vegetation interactions in submerged vegetation conditions. The hybrid model is used to overcome the local minima limitations of a feed-forward neural network. The prediction capability of model has been found to be better than past empirical predictors. The model developed herein showed significantly better results in several model performance criteria compared with empirical models.

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