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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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發(fā)表于 2025-3-21 18:14:38 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Artificial Neural Nets and Genetic Algorithms
期刊簡(jiǎn)稱Proceedings of the I
影響因子2023David W. Pearson,Nigel C. Steele,Rudolf F. Albrech
視頻videohttp://file.papertrans.cn/163/162615/162615.mp4
圖書封面Titlebook: Artificial Neural Nets and Genetic Algorithms; Proceedings of the I David W. Pearson,Nigel C. Steele,Rudolf F. Albrech Conference proceedin
影響因子Artificial neural networks and genetic algorithms both are areas of research which have their origins in mathematical models constructed in order to gain understanding of important natural processes. By focussing on the process models rather than the processes themselves, significant new computational techniques have evolved which have found application in a large number of diverse fields. This diversity is reflected in the topics which are subjects of the contributions to this volume.There are contributions reporting successful applications of the technology to the solution of industrial/commercial problems. This may well reflect the maturity of the technology, notably in the sense that ‘real‘ users of modelling/prediction techniques are prepared to accept neural networks as a valid paradigm. Theoretical issues also receive attention, notably in connection with the radial basis function neural network. Contributions in the field of genetic algorithms reflect the wide range of current applications, including, for example,portfolio selection, filter design, frequency assignment, tuning of nonlinear PID controllers. These techniques are also used extensively for combinatorial optimis
Pindex Conference proceedings 1995
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Workshop SummaryThe conference was preceded by a one day workshop. The morning was devoted to neural networks and the afternoon to genetic algorithms. General introductions, theoretical and implementational issues and specific applications were presented to the participants. We include here a brief summary of each workshop.
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Feminist Aesthetics in Contemporary Art,volution processes. The Bucket Brigade algorithm implements the first one, while the second one often use a genetic algorithm. Unfortunately, this kind of genetics-based machine learning systems suffers from a lot of problems yielding system instability, often resulting in poor performance. The main
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Feminist Aesthetics in Contemporary Art,rizontal probes in blast furnaces is a typical example. A neural network-based classification of such temperature measurements is described in this paper. Using temperature profiles first classified by the team, training and test sets were formed, and feedforward neural networks of different size we
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