Topic
Hybrid neural network
About: Hybrid neural network is a research topic. Over the lifetime, 1305 publications have been published within this topic receiving 18223 citations.
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TL;DR: A hybrid neural network is proposed to predict the energy performance of centrifugal pumps, where the theoretical loss model is incorporated into the back propagation neural network and then the neural network structure is optimized by automatically determining the node number of hidden layers.
36 citations
27 Nov 1995
TL;DR: An automated bake inspection system with artificial neural networks that utilises colour instead of monochrome images and a hybrid neural network of self-organising maps and FFNNs is proposed.
Abstract: The bake level of biscuits is of significant value to biscuit manufacturers as it determines the taste, texture and appearance of the products. Previous research explored and revealed the feasibility of biscuit bake inspection using feedforward neural networks (FFNN) with a backpropagation learning algorithm and monochrome images. A second study revealed the existence of a curve in colour space, called a baking curve, along which the bake colour changes during the baking process. Combining these results, the authors proposed an automated bake inspection system with artificial neural networks that utilises colour instead of monochrome images. In this paper, the authors present the implementation of the inspection system with a hybrid neural network of self-organising maps and FFNNs. The system was tested and its grading performance on biscuit bake levels was evaluated and compared to that of a trained human inspector. The authors found that the proposed colour system with a hybrid neural network performed significantly better than the human inspector.
36 citations
23 Jul 2000
TL;DR: In this article, a hybrid neural-network and expert system was proposed to increase the performance of automatic sleep stage scoring, and the result showed that the combination of computational and symbolic intelligence is promising approach to automatic sleep signal analysis.
Abstract: In order to increase the performance of automatic sleep stage scoring, we propose a hybrid neural-network and expert system taking advantages of each system. After signal cleaning and feature extraction from polysomnographic signals using several algorithms we suggested, the rule-based expert system classified the sleep states with symbolic reasoning. The neural network supplemented the shortcomings of rule-based system by dealing with exceptions of rules. The result shows that the combination of computational and symbolic intelligence is promising approach to automatic sleep signal analysis.
36 citations
01 Feb 2004
TL;DR: A hybrid neural network/genetic algorithm technique is presented, aiming at designing a feature extractor that leads to highly separable classes in the feature space.
Abstract: In this paper, a hybrid neural network/genetic algorithm technique is presented, aiming at designing a feature extractor that leads to highly separable classes in the feature space. The application upon which the system is built, is the identification of the state of human peripheral vascular tissue (i.e., normal, fibrous and calcified). The system is further tested on the classification of spectra measured from the cell nuclei in blood samples in order to distinguish normal cells from those affected by Acute Lymphoblastic Leukemia. As advantages of the proposed technique we may encounter the algorithmic nature of the design procedure, the optimized classification results and the fact that the system performance is less dependent on the classifier type to be used.
35 citations
TL;DR: Results show that the proposed EFHNN can be deployed effectively to sequential cash flow estimation and can assist project managers to control project cash flows within the banana envelope of the S-curve to enhance project success.
35 citations