Predicting Software Quality by Optimized BP Network Based on PSO

Li, Kewen; Kou, Jisong; Gong, Lina

doi:10.4304/jcp.6.1.122-129

Cited by 13 publications

(12 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The method that predicted the relationship between metric data and quality factors with historical data by using the optimized BP network based on PSO is proposed [12]. Reference [13] designed the PSO-PID controller for multi-leaf collimator, etc.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Design and Research on the LQR Controller for the Magnet Power Supply of the Accelerator based on Particle Swarm Optimization

Zhang

Dang

Wang

2015

IJHIT

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

“…The velocity of particle is defined as the distance of particle movement in each iteration, described as (12). 12 ( , , , )…”

mentioning

confidence: 99%

Design and Research on the LQR Controller for the Magnet Power Supply of the Accelerator based on Particle Swarm Optimization

Zhang

Dang

Wang

2015

IJHIT

View full text Add to dashboard Cite

show abstract

“…Prediction models are often used to find the non linear relationship between metric data and quality factors. This research study [8] predicted the relationship between metric data and quality factors with historical data by using the optimized BP network based on PSO. Experiments show that the algorithm has a better performance than the BP network algorithm and perfectly solve the problem of slow convergence and easily getting into local minimum.…”

Section: Related Work On Optimization Of Annmentioning

confidence: 99%

“…Researchers have shown the interest in the study of social insect's behavior in neural network area for solving different prediction problems. Several swarm intelligence algorithms have been proposed as training algorithms for ANNs such as Particle Swarm Optimization (PSO) [7,8,9,10], Bird Mating Optimizer (BMO) [13], Artificial Bee Colony [14], Ant colony Optimization [15,16], Cat Swarm Optimization [17], Genetic Algorithm [18,19], Artificial Fish Swarm Optimization [20], Cuckoo Search Optimization [21] Bacterial Foraging Optimization (BFO) [11,12]. In the last decade, evolutionary algorithms were proposed to fitness based methods as they are not sensitive to initial values and able to jump out of local minimal point.…”

Section: Related Work On Optimization Of Annmentioning

confidence: 99%

“…It is a continuous optimization problem. As a result, several algorithms have been proposed and applied as training algorithms for ANNs and these include Particle Swarm Optimization (PSO) [7,8,9,10], Bird Mating Optimizer (BMO) [13], Artificial Bee Colony [14], Ant colony Optimization [15,16], Cat Swarm Optimization [17], Genetic Algorithm [18,19], Artificial Fish Swarm Optimization [20], Cuckoo Search Optimization [21]. In this paper, the Bacterial Foraging Optimization (BFO) algorithm is proposed to be used as the training algorithm to train the ANNs.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Neural Network Learning Enhancement using Bacterial Foraging Optimization Algorithm

Kaur¹,

Kaur²

2014

IJCA

View full text Add to dashboard Cite

The artificial neural network (ANN) is a mathematical model capable of representing any non-linear relationship between input and output data. ANN is an abstract representation of the biological nervous system which has the ability to solve many complex problems. It has been successfully applied to a wide variety of classification and function approximation problems. The information processing capability of artificial neural networks (ANNs) is related to its architecture and weights. To have a high efficiency in ANN, selection of an appropriate architecture and learning algorithm is very important. In this study, the adaption of neural network connection weights using Bacterial Foraging Optimization Algorithm (BFO) is proposed as a mechanism to improve the performance of Artificial Neural Network in classification of Software Defect Dataset. The problem concerns the classification of software as defective or non defective on the basis of software metrics data. The results show that BFOANNs have better accuracy than traditional ANNs. The experimental results showed that BFOA-ANN has an improvement of 2.55 % in software defect prediction accuracy than the original feed forward artificial neural network and 2.80 % in case of cascade forward neural network.

show abstract

A Survey on Soft Computing Techniques for Spectrum Sensing in a Cognitive Radio Network

Eappen

Shankar

2020

SN COMPUT. SCI.

View full text Add to dashboard Cite

The need for faster wireless connectivity is increasing rapidly in all the sectors of the technologies. Whether it is a patient monitoring system, military application, entertainment services, streaming services, or global stock markets, there is a tremendous increase in the need for enhanced wireless telecommunication services. The wireless telecommunication consumers rely on bulk data, and massive growth in the number of users has resulted in the spectrum congestion. To avoid such spectrum congestion and to satisfy the data hunger of the wireless telecommunication users, the possible solution is Cognitive Radio Network (CRN). A CRN, therefore, plays a significant role in the field of wireless communication, and an efficient spectrum sensing enhances the effectiveness of the CRN. In this paper, complete research carried out so far in the field of spectrum sensing for CRN is discussed. Different soft computing techniques (GA, PSO, ABC, ACO, FFA, FSS, Cuckoo Search, ANN, FIS, GFIS) are surveyed in this paper, along with a detailed comparative analysis between conventional and soft computing techniques for spectrum sensing. In addition to that, the challenges faced in the implementation of CRN and its requirements is also addressed. Different spectrum sensing elements and requirements are presented and road map of spectrum sensing with soft computing techniques towards 5G is discussed. Furthermore, the paper also suggests the future prospects, research challenges and open issues associated with soft computing techniques for spectrum sensing in CRN.

show abstract

Predicting Software Quality by Optimized BP Network Based on PSO

Cited by 13 publications

References 13 publications

Design and Research on the LQR Controller for the Magnet Power Supply of the Accelerator based on Particle Swarm Optimization

Design and Research on the LQR Controller for the Magnet Power Supply of the Accelerator based on Particle Swarm Optimization

Artificial Neural Network Learning Enhancement using Bacterial Foraging Optimization Algorithm

A Survey on Soft Computing Techniques for Spectrum Sensing in a Cognitive Radio Network

Contact Info

Product

Resources

About