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Titlebook: Innovations in ART Neural Networks; Lakhmi C. Jain,Beatrice Lazzerini,Ugur Halici Book 2000 Springer-Verlag Berlin Heidelberg 2000 Adaptiv

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發(fā)表于 2025-3-21 16:10:22 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Innovations in ART Neural Networks
編輯Lakhmi C. Jain,Beatrice Lazzerini,Ugur Halici
視頻videohttp://file.papertrans.cn/468/467027/467027.mp4
叢書名稱Studies in Fuzziness and Soft Computing
圖書封面Titlebook: Innovations in ART Neural Networks;  Lakhmi C. Jain,Beatrice Lazzerini,Ugur Halici Book 2000 Springer-Verlag Berlin Heidelberg 2000 Adaptiv
描述In the last two decades the artificial neural networks have been refined and widely used by the researchers and application engineers. We have not witnessed such a large degree of evolution in any other artificial neural network as in the Adaptive Resonance Theory (ART) neural network. The ART network remains plastic, or adaptive, in response to significant events and yet remains stable in response to irrelevant events. This stability-plasticity property is a great step towards realizing intelligent machines capable of autonomous learning in real time environment..The main aim of this book is to report a very small sample of the research on the evolution of ART neural network and its applications. Interested readers may refer literature for many more innovations in ART such as Fuzzy ART, ART2, ART2-a, ARTMAP, ARTMAP-PI, ARTMAP-DS, Gaussian ARTMAP, EXACT ART, and ART-EMAP.
出版日期Book 2000
關(guān)鍵詞Adaptive Resonance Theory; Extension; Fuzzy Logik; Mapping; algorithms; artificial neural network; evoluti
版次1
doihttps://doi.org/10.1007/978-3-7908-1857-4
isbn_softcover978-3-7908-2469-8
isbn_ebook978-3-7908-1857-4Series ISSN 1434-9922 Series E-ISSN 1860-0808
issn_series 1434-9922
copyrightSpringer-Verlag Berlin Heidelberg 2000
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Novel Extension of ART2 in Surface Landmine Detection,ird input. In real-time situations these two classifier outputs indicate the likelihood of a surface landmine target when presented with a number of multispectral and textural bands. Due to the modifications in ART2, this updated alternative architecture has improved real-time landmine detection capabilities.
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,Art-Based Autonomous Learning Systems: Part I — Architectures and Algorithms,ntal, causal learning as well as for Bayesian probability estimation. Subsequently, a multiple neural network architecture is devised to aggregate outputs from several individual networks into a unified decision. A number of algorithms is proposed to increase the generalization and adaptability of the resulting systems.
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