An-FPGA Based Classification System by Using a Neural Network and an Improved Particle Swarm Optimization Algorithm

Dang, Tuan Linh; Hoshino, Yukinobu

doi:10.1109/scis-isis.2016.0033

Cited by 6 publications

(8 citation statements)

References 12 publications

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“…As mentioned by [18], [19], the PSO is a very promising searching algorithm which has a weakness when it comes to performing local search to attain the global minimum. Although several improvements have been proposed to improve the PSO algorithm over the years, which includes the MPSO [40] and PSOBP [35] algorithms which we have covered in Section II, the capability of the PSO to perform on its own remains an issue and the tendency to become trapped in some undesirable local minimum increases when size of the dimension to be optimized increases. This would become a problem in this research as the size of the dimension increases as the value of www.ijacsa.thesai.org is increased, due to the increase in number of synaptic weights in the ANN.…”

Section: A Proposed Methodologymentioning

confidence: 99%

“…Seed factors of all particles are randomly generated when the algorithm is initialized, and would help to pull the particles to the initial positions of the seeds. The main purpose of adding the seed factor as a third component in the velocity update equation is to help reduce the possibility of the PSO algorithm from getting trapped in a local minimum [40]. The new velocity update equation used in the MPSO algorithm is shown below:…”

Section: Existing Work On Pso With Annmentioning

confidence: 99%

“…After studying all three papers, several gaps in the literature have been identified, as shown in Table 2. Firstly, all the authors, Armaghani et al [17], Dang & Hoshino [40] and Koohi & Groza [35] included the implementation of PSO for weight training purposes and did not implement any form of ANN topology optimization. In their studies, the best ANN topology is still decided by using trial and error, with multiple ANN models of varying topologies being developed and trained to find the one with the best performance.…”

Section: Existing Work On Pso With Annmentioning

confidence: 99%

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Adaptive Multilayered Particle Swarm Optimized Neural Network (AMPSONN) for Pipeline Corrosion Prediction

Lee¹,

Aziz²,

Jaafar³

2017

ijacsa

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Abstract-Artificial Neural Network (ANN) design has long been a complex problem because its performance depends heavily on the network topology and algorithm to train the set of synaptic weights. Particle Swarm Optimization (PSO) has been the favored optimization algorithm to complement ANN, but a thorough literature study has shown that there are gaps with current approaches which integrate PSO with ANN, including the optimization of network topology and the unreliable weight training process. These gaps have caused inferior effect on critical Artificial Intelligence (AI) applications and systems, particularly when predicting plant machinery and piping failure due to corrosion. The problem of corrosion prediction in the oil and gas domain remains unanswered due to the lack of a flexible prediction method which targets specific damage mechanisms that caused corrosion. This paper proposes a hybrid prediction method known as the Adaptive Multilayered Particle Swarm Optimized Neural Network (AMPSONN), which integrates several layers of PSO to optimize different parameters of the ANN. The multilayered PSO enables the method to optimize the network topology and train the set of synaptic weights at the same time using a hierarchical optimization approach. Through detailed discussion and literature study, the damage mechanism focused in this research is the CO 2 corrosion and the dataset for this research is obtained from the NORSOK empirical model. The proposed AMPSONN method is tested against BP, MPSO and PSOBP methods on an industrial corrosion dataset for different test conditions. The results showed that AMPSONN performs best on all three problems, exhibiting high classification accuracies and time efficiency.

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Section: A Proposed Methodologymentioning

confidence: 99%

Section: Existing Work On Pso With Annmentioning

confidence: 99%

Section: Existing Work On Pso With Annmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Multilayered Particle Swarm Optimized Neural Network (AMPSONN) for Pipeline Corrosion Prediction

Lee¹,

Aziz²,

Jaafar³

2017

ijacsa

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“…On the other hand, the NN is implemented in FPGA to increase the operation speed. The NN is implemented in FPGA so that the hardware speed could be maintained [15][16][17]. In the previous study, an improved version of the SPSO was proposed to solve the problem of the local minimum called the PSOd − CV algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…The wPSOd − CV is also proposed in this section. The NN-wPSOd − CV is based on the NN-PSO framework presented in the previous papers [15][16][17]. Section…”

Section: Introductionmentioning

confidence: 99%

Improved PSO Algorithm for Training of Neural Network in Co-design Architecture

Dang¹,

Hoshino²

2019

IJCA

Self Cite

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This paper proposes a new version of the standard particle swarm optimization (SPSO) algorithm to train a neural network (NN). The improved PSO, called the wPSOd − CV algorithm, is the improved version of the PSOd − CV algorithm presented in a previous study. The wPSOd − CV algorithm is introduced to solve the issue of premature convergence of the SPSO algorithm. The proposed wPSOd − CV algorithm is used in a co-design architecture. Experimental results confirmed the effectiveness of the NN trained by the wPSOd − CV algorithm when compared with the NN trained by the SPSO algorithm and the PSOd − CV algorithm concerning the minimum learning error and the recognition rates.

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Optimized Intelligent PID Controller for Propofol Dosing in General Anesthesia Using Coati Optimization Algorithm

Namel,

Sahib

2024

Communications in Computer and Information Science

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An-FPGA Based Classification System by Using a Neural Network and an Improved Particle Swarm Optimization Algorithm

Cited by 6 publications

References 12 publications

Adaptive Multilayered Particle Swarm Optimized Neural Network (AMPSONN) for Pipeline Corrosion Prediction

Adaptive Multilayered Particle Swarm Optimized Neural Network (AMPSONN) for Pipeline Corrosion Prediction

Improved PSO Algorithm for Training of Neural Network in Co-design Architecture

Optimized Intelligent PID Controller for Propofol Dosing in General Anesthesia Using Coati Optimization Algorithm

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