By Christopher G. Langton
Man made existence is the research of artificial platforms that express behaviors attribute of common residing structures, reminiscent of self-organization, replica, improvement, or even evolution. It enhances the normal organic sciences interested by the research of residing organisms by way of trying to synthesize and examine life-like behaviors inside of desktops or different ”alternative” media. through extending the empirical beginning upon which biology rests past the carbon-chain dependent lifestyles that has developed in the world, man made existence can give a contribution to the theoretical biology by way of finding ”life-as-we-know-it” in the better context of ”life-as-it-could-be,” in any of its attainable actual incarnations.
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Extra resources for Artificial life III (Santa Fe Institute Studies in the Sciences of Complexity Proceedings)
To represent and predict the influence of various time-varying environmental factors on tidal changes, the proposed modular prediction approach is implemented for real-time tidal level prediction. The model is composed of the mechanism module which is performed by conventional harmonic tidal prediction method, and a neural network module which is realized by ELM based on SDW (SDW-ELM) which is real-time updated. The SDW-ELM adjusts network structure and connecting parameters by learning samples in the sliding window sequentially, and makes predictions simultaneously.
However, these influences cannot be reflected in tidal table, which is the foundation for seafarers to make voyage planning . Therefore, environmental influences would be ignored even if they may cause accidents such as grounding on rock. Furthermore, these meteorological factors are time-varying in nature, which is hard to be represented by strictly founded mathematical model. Therefore, there is a need to construct an adaptive model whose structure and parameters can adapt to the above-mentioned time-varying environmental changes.
References 1. -B. H. Wang, Y. Lan, Extreme learning machines: a survey. Int. J. Mach. Learn. Cybernet. 2(2), 107–122 (2011) 2. -B. -Y. -K. Siew, Extreme learning machine: a new learning scheme of feedforward neural networks, in Proceedings. 2004 IEEE International Joint Conference on Neural Networks, 2004, vol. 2, pp. 985–990, IEEE, 2004 3. -B. -Y. -K. Siew, Extreme learning machine: theory and applications. Neurocomputing. 70(1), 489–501 (2006) 4. W. Zong, H. -B. Huang, Z. Lin, Face recognition based on kernelized extreme learning machine, in Autonomous and Intelligent Systems (Springer, Berlin, 2011) pp.
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