Open AccessProceedings Article
The Cascade-Correlation Learning Architecture
Scott E. Fahlman,Christian Lebiere +1 more
- Vol. 2, pp 524-532
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TLDR
The Cascade-Correlation architecture has several advantages over existing algorithms: it learns very quickly, the network determines its own size and topology, it retains the structures it has built even if the training set changes, and it requires no back-propagation of error signals through the connections of the network.Abstract:
Cascade-Correlation is a new architecture and supervised learning algorithm for artificial neural networks. Instead of just adjusting the weights in a network of fixed topology. Cascade-Correlation begins with a minimal network, then automatically trains and adds new hidden units one by one, creating a multi-layer structure. Once a new hidden unit has been added to the network, its input-side weights are frozen. This unit then becomes a permanent feature-detector in the network, available for producing outputs or for creating other, more complex feature detectors. The Cascade-Correlation architecture has several advantages over existing algorithms: it learns very quickly, the network determines its own size and topology, it retains the structures it has built even if the training set changes, and it requires no back-propagation of error signals through the connections of the network.read more
Citations
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Journal ArticleDOI
Neural Network Analysis of Chloride Diffusion in Concrete
Jun Peng,Zongjin Li,Baoguo Ma +2 more
TL;DR: In this article, the authors used a cascade-correlation algorithm to predict the chloride profiles diffused through concrete specimens, and found that the predictions given by the cascade correlation algorithm are in good agreement with the test results in both steady and unsteady states.
Journal ArticleDOI
Development and evaluation of the cascade correlation neural network and the random forest models for river stage and river flow prediction in Australia
Mohammad Ali Ghorbani,Ravinesh C. Deo,Sungwon Kim,Mahasa Hasanpour Kashani,Vahid Karimi,Maryam Izadkhah +5 more
TL;DR: Artificial intelligence techniques, namely the cascade correlation neural networks (CCNN) and the random forest models, were employed in daily river stage and river flow prediction for two river systems in Australia, and it was ascertained that the CCNN model can be taken as a preferred data intelligent tool for river stage
Posted Content
A Learning Algorithm for Evolving Cascade Neural Networks
TL;DR: A new learning algorithm for Evolving Cascade Neural Networks (ECNNs) is described and was successfully applied to classify artifacts and normal segments in clinical electroencephalograms (EEGs).
Patent
Method and apparatus for discovering evolutionary changes within a system
David E. Huddleston,Yoh-Han Pao,Ronald Cass,Qian Yang,Ella Polyak,Peter Cryer,Charles Edward Garofalo +6 more
TL;DR: In this article, an adaptive system model is generated by using data corresponding to an input features set selected by using a baseline significance signature of the system, and data collected from the system corresponding to the superset is maintained online.
Proceedings ArticleDOI
Fast and efficient incremental learning for high-dimensional movement systems
Sethu Vijayakumar,Stefan Schaal +1 more
TL;DR: This work introduces a new algorithm, locally weighted projection regression (LWPR), for incremental real-time learning of nonlinear functions, as particularly useful for problems of autonomous real- time robot control that requires internal models of dynamics, kinematics, or other functions.
References
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