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Titlebook: Artificial Neural Nets and Genetic Algorithms; Proceedings of the I David W. Pearson,Nigel C. Steele,Rudolf F. Albrech Conference proceedin

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期刊全称Artificial Neural Nets and Genetic Algorithms
期刊简称Proceedings of the I
影响因子2023David W. Pearson,Nigel C. Steele,Rudolf F. Albrech
视频video
发行地址Latest developments in neural nets and genetic algorithms
图书封面Titlebook: Artificial Neural Nets and Genetic Algorithms; Proceedings of the I David W. Pearson,Nigel C. Steele,Rudolf F. Albrech Conference proceedin
影响因子The 2003 edition of ICANNGA marks a milestone in this conference series, because it is the tenth year of its existence. The series began in 1993 with the inaugural conference at Innsbruck in Austria. At that first conference, the organisers decided to organise a similar scientific meeting every two years. As a result, conferences were organised at Ales in France (1995), Norwich in England (1997), Portoroz in Slovenia (1999) and Prague in the Czech Republic (2001). It is a great honour that the conference is taking place in France for the second time. Each edition of ICANNGA has been special and had its own character. Not only that, participants have been able to sample the life and local culture in five different European coun­ tries. Originally limited to neural networks and genetic algorithms the conference has broadened its outlook over the past ten years and now includes papers on soft computing and artificial intelligence in general. This is one of the reasons why the reader will find papers on fuzzy logic and various other topics not directly related to neural networks or genetic algorithms included in these proceedings. We have, however, kept the same name, "International Co
Pindex Conference proceedings 2003
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A learning probabilistic neural network with fuzzy inference,roposed. The advantages of this network lie in the possibility of classification of the data with substantially overlapping clusters, and tuning of the activation function parameters improves the accuracy of classification. Simulation results confirm the efficiency of the proposed approach in the da
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A hybrid algorithm for weight and connectivity optimization in feedforward neural networks,or performance because of lack of expressional capacity, while a too large network fits noise or apparent relations in the data sets studied. The work required to find a parsimonious network is often considerable with respect to both time and computational effort. This paper presents a method for tr
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Binary Factorization in Hopfield-Like Neural Autoassociator: A Promising Tool for Data Compression,n feature extraction procedure which maps original patterns into features (factors) space of reduced, possibily very small, dimension. In this paper, we outline that Hebbian unsupervised learning of Hopfield-like neural network is a natural procedure for factor extraction. Due to this learning, fact
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