AINet-CM
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An AINet-CM is an AiNet System based on Cloud Model that implements an AINet-CM Algorithm to solve an AINet-CM Task.
- Example(s):
- …
- Counter-Example(s):
- See: Immune Network, Artificial Immune Network Topology, Neural Network, Machine Learning System, Clonal Selection System.
References
2017
- (Wang et al., 2017) ⇒ Mingan Wang, Shuo Feng, Jianming Li, Zhonghua Li, Yu Xue, and Dongliang Guo (2017). "Cloud Model-Based Artificial Immune Network for Complex Optimization Problem". Computational intelligence and neuroscience, 2017. DOI: 10.1155/2017/5901258
- QUOTE: Cloud model [1] is a conversion model with certainty between a qualitative concept and a quantitative number expression. Up to date, cloud model has been applied in many fields [2] [3]due to its randomness and stability. For example, in intelligent computation, several cloud model-based algorithms — the cloud-based adaptive genetic algorithm (CAGA) [4], asymmetrical cloud model-based genetic algorithm (ACGA) [5], and particle swarm optimization with normal cloud model (CPSO) [6] — have been developed. It is clearly shown that the combination of the cloud model and evolutionary algorithms is of interest to researchers and engineers. The article [7] proposes an artificial immune network based on the cloud model (AINet-CM), where the cloud models are used to evaluate the candidate antibodies. Different cloud models are embedded into three major immune operators — clone, mutation, and suppression — to enhance the algorithmic convergence. As an extensive study of , this paper will systematically investigate the cloud-based operators of AINet-CM and examine the convergence and accuracy of AINet-CM by evaluating unimodal or multimodal functions whose dimension is 2D, 10D, and 30D. In addition, two kinds of typical applied experiments — band-pass FIR filter designing and industrial PID controller optimization — are arranged to demonstrate the effectiveness and high-performance of AINet-CM.
- ↑ Li D. Y., Meng H. J., Shi X. M. Membership clouds and membership cloud generators. Journal of Computer Research and Development. 1995;32:15–20
- ↑ Wang H., He S., Liu X., et al. Simulating urban expansion using a cloud-based cellular automata model: a case study of Jiangxia, Wuhan, China. Landscape and Urban Planning. 2013;110(1):99–112. doi: 10.1016/j.landurbplan.2012.10.016
- ↑ Jiang Y., Wang X., Lin F. Voice communication network quality of service estimation and forecast based on cloud model. Applied Mechanics and Materials. 2013;284–287:3463–3467. doi: 10.4028/www.scientific.net/AMM.284-287.3463
- ↑ Jiang Y., Jiang J., Zhang Y. A novel fuzzy multiobjective model using adaptive genetic algorithm based on cloud theory for service restoration of shipboard power systems. IEEE Transactions on Power Systems. 2012;27(2):612–620. doi: 10.1109/TPWRS.2011.2179951
- ↑ Fu Q., Cai Z.-H., Wu Y.-Q. A novel hybrid method: genetic algorithm based on asymmetrical cloud model. Proceedings of the 2010 International Conference on Artificial Intelligence and Computational Intelligence, AICI 2010; October 2010; pp. 445–449.
- ↑ Li M.-W., Hong W.-C., Kang H.-G. Urban traffic flow forecasting using Gauss-SVR with cat mapping, cloud model and PSO hybrid algorithm. Neurocomputing. 2013;99:230–240. doi: 10.1016/j.neucom.2012.08.002.
- ↑ Li Z., Li J., Guo D., Yang Z. A cloud-based artificial immune network for optimization. Proceedings of the 9th International Conference on Natural Computation, ICNC 2013; July 2013; pp. 628–633.