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 novel hybrid neural network is proposed to solve monocular depth estimation problem, and concurrently a dense depth map is predicted from the monocular still image and a novel logarithm exponential average error (LEAE) is propose to overcome over-weighted problem.
Abstract: Depth information from still 2D images plays an important role in automated driving, driving safety, and robotics. Monocular depth estimation is considered as an ill-posed and inherently ambiguous ...
5 citations
19 Mar 2008
TL;DR: Ordered FAMDDA, in general, outperforms FAM and Ordered FAM in tackling pattern classification problems and is able to reduce/resolve overlapping regions of different classes in the feature space for minimizing misclassification during the network learning phase.
Abstract: This paper presents a novel conflict-resolving neural network classifier that combines the ordering algorithm, fuzzy ARTMAP (FAM), and the dynamic decay adjustment (DDA) algorithm, into a unified framework. The hybrid classifier, known as Ordered FAMDDA, applies the DDA algorithm to overcome the limitations of FAM and ordered FAM in achieving a good generalization/performance. Prior to network learning, the ordering algorithm is first used to identify a fixed order of training patterns. The main aim is to reduce and/or avoid the formation of overlapping prototypes of different classes in FAM during learning. However, the effectiveness of the ordering algorithm in resolving overlapping prototypes of different classes is compromised when dealing with complex datasets. Ordered FAMDDA not only is able to determine a fixed order of training patterns for yielding good generalization, but also is able to reduce/resolve overlapping regions of different classes in the feature space for minimizing misclassification during the network learning phase. To illustrate the effectiveness of Ordered FAMDDA, a total of ten benchmark datasets are experimented. The results are analyzed and compared with those from FAM and Ordered FAM. The outcomes demonstrate that Ordered FAMDDA, in general, outperforms FAM and Ordered FAM in tackling pattern classification problems.
5 citations
09 Nov 2020
TL;DR: Groundwater is the world's central supply of fresh water and water supply policies, particularly in dry seasons, need to be based on accurate modelling of water level fluctuations, according to the study report.
Abstract: Groundwater is the world's central supply of fresh water. Water supply policies, particularly in dry seasons, thus need to be based on accurate modelling of groundwater level (GWL) fluctuations. In...
5 citations
Proceedings Article•
02 Dec 1991
TL;DR: A novel application of neural networks to system health monitoring of a large antenna for deep space communications using hybrid signal processing and neural network techniques, including autoregressive modelling, pattern recognition, and Hidden Markov models is described.
Abstract: We describe in this paper a novel application of neural networks to system health monitoring of a large antenna for deep space communications. The paper outlines our approach to building a monitoring system using hybrid signal processing and neural network techniques, including autoregressive modelling, pattern recognition, and Hidden Markov models. We discuss several problems which are somewhat generic in applications of this kind - in particular we address the problem of detecting classes which were not present in the training data. Experimental results indicate that the proposed system is sufficiently reliable for practical implementation.
5 citations
TL;DR: A neural network architecture is introduced which implements a supervised clustering algorithm for the classification of feature vectors that combines the strengths of three wellknown architectures: learning vector quantisation, backpro-pagation and adaptive resonance theory.
Abstract: A neural network architecture is introduced which implements a supervised clustering algorithm for the classification of feature vectors. The network is selforganising, and is able to adapt to the shape of the underlying pattern distribution as well as detect novel input vectors during training. It is also capable of determining the relative importance of the feature components for classification. The architecture is a hybrid of supervised and unsupervised networks, and combines the strengths of three wellknown architectures: learning vector quantisation, backpro-pagation and adaptive resonance theory. Network performance is compared to that of learning vector quantisation, back-propagation and cascade-correlation. It is found that performance is generally as good as or better than the performance of these other architectures, while training time is considerably shorter. However, the main advantage of the hybrid architecture is its ability to gain insight into the feature pattern space.
5 citations