GA-based polynomial neural networks architecture and its application to multi-variable software process

Authors:
Sung-Kwun Oh;Witold Pedrycz;Wan-Su Kim;Hyun-Ki Kim
Affiliations:
Department of Electrical Engineering, The University of Suwon, Hwaseong-si, Gyeonggi-do, Korea;Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada and Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland;Department of Electrical Engineering, The University of Suwon, Hwaseong-si, Gyeonggi-do, Korea;Department of Electrical Engineering, The University of Suwon, Hwaseong-si, Gyeonggi-do, Korea
Venue:
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Year:
2006

Citing 8
Cited 0

Genetic algorithms + data structures = evolution programs (3rd ed.)

Genetic algorithms + data structures = evolution programs (3rd ed.)
Handbook of software reliability engineering

Handbook of software reliability engineering
Identification of fuzzy systems by means of an auto-tuning algorithm and its application to nonlinear systems

Fuzzy Sets and Systems
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
The design of self-organizing polynomial neural networks

Information Sciences—Informatics and Computer Science: An International Journal
Fuzzy function approximation with ellipsoidal rules

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Relation-based neurofuzzy networks with evolutionary data granulation

Mathematical and Computer Modelling: An International Journal
Comparison of adaptive methods for function estimation from samples

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we propose a architecture of Genetic Algorithms (GAs)-based Polynomial Neural Networks(PNN), discuss a comprehensive design methodology and carry out a series of numeric experiments. GA-based design procedure at each stage (layer) of PNN leads to the selection of preferred nodes (or PNs) with optimal parameters (such as the number of input variables, input variables, and the order of the polynomial) available within PNN. A detailed design procedure is discussed in detail. To evaluate the performance of the GA-based PNN, the model is experimented with by using Medical Imaging System (MIS) data for application to Multi-variable software process. A comparative analysis shows that the proposed GA-based PNN is model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.