Identification of impurity in wheat mass based on video processing using artificial neural network and PSO algorithm

The movement of currency exchange rate can be predicted in the next few days, this is used by economic actors to get profit. Artificial Neural Network with the backpropagation learning method is good enough to use for forecasting time series data, it's just that in its application this method was considered to have shortcomings such as a long training time to achieve convergence. The purpose of this research is to form a Multilayer Perceptron Artificial Neural Network model with the Particle Swarm Optimization (PSO) algorithm as a learning method in the case of currency exchange rate prediction. This research produced a model that can predict the movement of the Rupiah exchange rate against the US Dollar, while the model formed was the MLP-PSO model with an error rate of 5.6168 x 10-8, slightly better than the MLP-BP model with an error rate of 6.4683 x 10-8. These results indicated that the PSO algorithm can be used as a learning algorithm in the Multilayer Perceptron Artificial Neural Network.

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Biometric authentication of physical characteristics recognition using artificial neural network with PSO algorithm

International Journal of Computer Applications in Technology ◽

10.1504/ijcat.2017.10008703 ◽

2017 ◽

Vol 56 (3) ◽

pp. 219

Author(s):

Lazarus Nisha Evangelin ◽

Alfred Lenin Fred

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Pso Algorithm ◽

Physical Characteristics ◽

Biometric Authentication ◽

Artificial Neural

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Designing a Fruit Identification Algorithm in Orchard Conditions to Develop Robots Using Video Processing and Majority Voting Based on Hybrid Artificial Neural Network

Applied Sciences ◽

10.3390/app10010383 ◽

2020 ◽

Vol 10 (1) ◽

pp. 383 ◽

Cited By ~ 2

Author(s):

Sajad Sabzi ◽

Razieh Pourdarbani ◽

Davood Kalantari ◽

Thomas Panagopoulos

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Video Processing ◽

Color Space ◽

Majority Voting ◽

Identification Algorithm ◽

Natural Conditions ◽

Artificial Neural ◽

Majority Voting Method ◽

Voting Method

The first step in identifying fruits on trees is to develop garden robots for different purposes such as fruit harvesting and spatial specific spraying. Due to the natural conditions of the fruit orchards and the unevenness of the various objects throughout it, usage of the controlled conditions is very difficult. As a result, these operations should be performed in natural conditions, both in light and in the background. Due to the dependency of other garden robot operations on the fruit identification stage, this step must be performed precisely. Therefore, the purpose of this paper was to design an identification algorithm in orchard conditions using a combination of video processing and majority voting based on different hybrid artificial neural networks. The different steps of designing this algorithm were: (1) Recording video of different plum orchards at different light intensities; (2) converting the videos produced into its frames; (3) extracting different color properties from pixels; (4) selecting effective properties from color extraction properties using hybrid artificial neural network-harmony search (ANN-HS); and (5) classification using majority voting based on three classifiers of artificial neural network-bees algorithm (ANN-BA), artificial neural network-biogeography-based optimization (ANN-BBO), and artificial neural network-firefly algorithm (ANN-FA). Most effective features selected by the hybrid ANN-HS consisted of the third channel in hue saturation lightness (HSL) color space, the second channel in lightness chroma hue (LCH) color space, the first channel in L*a*b* color space, and the first channel in hue saturation intensity (HSI). The results showed that the accuracy of the majority voting method in the best execution and in 500 executions was 98.01% and 97.20%, respectively. Based on different performance evaluation criteria of the classifiers, it was found that the majority voting method had a higher performance.

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Predicting Shear Strength in FRP-Reinforced Concrete Beams Using Bat Algorithm-Based Artificial Neural Network

Advances in Materials Science and Engineering ◽

10.1155/2021/5899356 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Mohammad Nikoo ◽

Babak Aminnejad ◽

Alireza Lork

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Shear Strength ◽

Reinforced Concrete ◽

Concrete Beams ◽

Bat Algorithm ◽

Pso Algorithm ◽

Reinforced Concrete Beams ◽

Feed Forward Network ◽

Artificial Neural

In this article, 140 samples with different characteristics were collected from the literature. The Feed Forward network is used in this research. The parameters f’c (MPa), ρf (%), Ef (GPa), a/d, bw (mm), d (mm), and VMA are selected as inputs to determine the shear strength in FRP-reinforced concrete beams. The structure of the artificial neural network (ANN) is also optimized using the bat algorithm. ANN is also compared to the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm. Finally, Nehdi et al.’s model, ACI-440, and BISE-99 equations were used to evaluate the models’ accuracy. The results confirm that the bat algorithm-optimized ANN is more capable, flexible, and provides superior precision than the other three models in determining the shear strength of the FRP-reinforced concrete beams.

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PSO-BP Combined Artificial Neural Network Method Research

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.353-356.3537 ◽

2013 ◽

Vol 353-356 ◽

pp. 3537-3540

Author(s):

Gui Ling Liu ◽

Feng Gao

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Local Minimum ◽

Pso Algorithm ◽

Gradient Algorithm ◽

Ann Model ◽

Error Surface ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Searching Ability

BP artificial neural network(ANN) based on gradient algorithm method is widely applied, but because the error surface of object function is very complex and the choose of initial value effects network training results, convergence rate is slow and local minimum is likely to fall into. Particle swarm optimization(PSO) algorithm has better global searching ability to get rid the puzzles of falling into local minimum. By adequately studying on the two algorithms’ characteristics, a new type of combined ANN training method is put forward, and PSO-BP ann model is successfully built.

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