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.

Analysis of chemical exposure through inhalation using hybrid neural network

Abstract: In this analysis, human health risk through inhalation due to exposure to Benzene from vehicular emissions in New Zealand is assessed as an example of the application of a hybrid neural network. Exposure factors affecting the inhalation are inhaled contaminant, age, body weight, health status and activity patterns of humans. There are four major variables affecting the inhaled contaminant viz., gas emissions from motor vehicles on the road, wind speed, temperature and atmospheric stability. The topic of uncertainty applies equally to all variables involved in exposure analysis. Neural network and fuzzy theory is implemented to solve the uncertainty, which exists to a greater extent. The architecture of hybrid neural network that is used to estimate the exposure of carcinogens through inhalation is explained in detail in this paper.

Voice signal feature fusion method

Abstract: The invention discloses a novel voice signal feature fusion method. The method comprises the following steps: S1, constructing a model framework; S2, designing a neural network; and S3, carrying out audio feature representation and extraction. According to the method, a CNN variant and an LSTM variant are parallelly fused to form the novel hybrid neural network, meanwhile, the signal features having the maximal influences on the final emotion are extracted by combining with an Attention mechanism, finally, the audio emotion vector capable of more accurately and more rapidly classifying emotionis obtained, the generalization ability is strong, the structure is clear, the integration with or separation from other modules is easy, and in addition, the problem that the traditional voice signal features can not realize effective fusion is solved.

Development of Genetic Algorithm Based Macro Mechanical Model for Steel Fibre Reinforced Concrete

Abstract: This paper presents the applicability of hybrid networks that combine Artificial Neural Network (ANN) and Genetic Algorithm (GA) for predicting the strength properties of Steel Fibre Reinforced concrete (SFRC) with different water-cement ratio (0.4,0.45,0.5,0.55), aggregate-cement ratio (3,4,5), % of fibres (0.75,1.0,1.5) and aspect ratio of fibres (40,50,60) as input vectors. Strength properties of SFRC such as compressive strength, flexural strength, split tensile strength and compaction factor are considered as output vector. The network has been trained with data obtained from experimental work. The hybrid neural network model learned the relation between input and output vectors in 1900 iterations. After successful learning GA based BPN model predicted the strength characteristics satisfying all the constrains with an accuracy of about 95%.The various stages involved in the development of genetic algorithm based neural network model are addressed at length in this paper.

Hybrid Neural Network Based Risk Assessment Method for Large Scale Sports Events

Abstract: This paper focuses on the problem of risk assessment method for large scale sports events which is an important problem in modern sports management. The index system for large scale sports events risk assessment is proposed in advance, which is made up of three categories: 1) risk before match, 2) risk in match and 3) risk after match. Particularly, eighteen influencing factors are design which can cover all aspects of the large scale sports events risk assessment. Structure of the hybrid neural network is contructed by three layers, which are "the input layer", "the hidden layer", and "the output layer". Particularly, the output layers can compute the regression function values, and the regression function of the artificial neural network can be computed through a linear integration of some nonlinear basis functions. Afterwards, utilizing a training dataset and its updating version, values of the decision functions of the multi-class support vector classifier can be obtained by the regression functions based on the artificial neural network. Finally, experiments are conducted to test the effectiveness of our algorithm. The conclusions can be drawn that compared with the artificial neural network, the proposed hybrid neural network is more suitable for the risk assessment of large scale sports events.