Book ChapterDOI
Research a Novel Optimization Mechanism of Parameters Based on Hybrid NN and GA
Yansong Liu,Rulong Wang,Gang Yi +2 more
- pp 451-460
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TLDR
It has been indicated that the injection parameter optimization method based on the hybrid neural network and genetic algorithm approach is feasible and the BP network is stable and reliable.Abstract:
In this paper, an optimization system is established based on a hybrid neural network and genetic algorithm approach. The application program is compiled in Matlab engineering computing language, which is used in calculating the parameter value predicted by neural network and the result of genetic algorithm optimization .The comparison and error analysis has been carried out between the results predicted by network and CAE simulated results, which shows that the BP network is stable and reliable. The optimized outcome verified by CAE simulation and tested by experiment has been proved to be correct. It has been bean indicated that the injection parameter optimization method based on the hybrid neural network and genetic algorithm approach is feasible.read more
References
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Performance evaluation of some clustering algorithms and validity indices
TL;DR: This article evaluates the performance of three clustering algorithms, hard K-Means, single linkage, and a simulated annealing (SA) based technique, in conjunction with four cluster validity indices, namely Davies-Bouldin index, Dunn's index, Calinski-Harabasz index, andA recently developed index I.
Journal ArticleDOI
A hybrid neural network and genetic algorithm approach to the determination of initial process parameters for injection moulding
S.L. Mok,C. K. Kwong,W.S. Lau +2 more
TL;DR: In this article, a hybrid neural network and genetic algorithm approach is described to determine a set of initial process parameters for injection molding, which is based on a skilled operator's know-how and intuitive sense acquired through long-term experience rather than on a theoretical and analytical approach.
Journal ArticleDOI
Prediction of processing parameters for injection moulding by using a hybrid neural network
TL;DR: The accuracy of the developed network has been tested by predicting the injection pressure and injection time for few engineering components and showed that the Levenberg algorithm converged rapidly with fewer training cycles when compared with the error back-propagation algorithm.
A simulation study on load modeling of a thermal power unit based on wavelet neural networks
TL;DR: A simulation study on load modeling of a thermal power unit by a kind of multi-input-multi-output continual WNN model, where the linear function and wavelet basis function satisfying the frame condition are employed as an activation function in output and hidden layer respectively.
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