By Anastasios Tefas, Alexandros Iosifidis, Ioannis Pitas (auth.), Lazaros Iliadis, Harris Papadopoulos, Chrisina Jayne (eds.)
The volumes set, CCIS 383 and 384, constitutes the refereed court cases of the 14th overseas convention on Engineering functions of Neural Networks, EANN 2013, hung on Halkidiki, Greece, in September 2013. The ninety one revised complete papers offered have been conscientiously reviewed and chosen from a number of submissions. The papers describe the functions of man-made neural networks and different gentle computing methods to varied fields corresponding to development recognition-predictors, delicate computing purposes, scientific purposes of AI, fuzzy inference, evolutionary algorithms, class, studying and knowledge mining, keep an eye on techniques-aspects of AI evolution, picture and video research, class, trend acceptance, social media and neighborhood dependent governance, clinical purposes of AI-bioinformatics and learning.
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Extra info for Engineering Applications of Neural Networks: 14th International Conference, EANN 2013, Halkidiki, Greece, September 13-16, 2013 Proceedings, Part I
This paper proposes a hybrid stochastic competitive Hopﬁeld neural network-eﬃcient genetic algorithm (SCH-EGA) approach to tackle the frequency assignment problem (FAP). The objective of FAP is to minimize the cochannel interference between satellite communication systems by rearranging the frequency assignments so that they can accommodate the increasing demands. In fact, as SCH-EGA algorithm owns the good adaptability, it can not only deal with the frequency assignment problem, but also cope with the problems of clustering, classiﬁcation, the maximum clique problem and so on.
1 Table 6. No. 1 Conclusion In this paper, a robust and highly efficient MPEG-4 video frame estimator is presented that uses a Neuro-Evolutionary algorithm called R-CGPANN. R-CGPANN uses recurrent nodes in its network and has a measurable performance edge over its feedforward counterpart. The estimating system is deployed that takes 10 previously received frames as its input and based on that information, the next frame size is predicated. A variety of experiments are conducted to analyze the efficiency of the system.
Efficient reinforcement learning through evolving neural networks topologies. In: GECCO 2002 (2002) 7. : Multi-Chromosomal Representation in NeuroEvolution. In: Computations Intelligence Conference (2006) 8. : Interactive Evolutionary Computation: Fusion of the Capabilities of EC Optimization and Human Evaluation. Proc. of the IEEE 89(9), 1275–1296 (2001) 9. : Evolino: Hybrid Neuroevolution Optimal Linear Search for Sequence Prediction. In: 19th Int. Conf. on Artificial Intelligence (2005) 10.