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.

Hybrid Neural Network Prediction Model of Hydraulic Valve Characteristics Under the Affection of Multiple Geometric Factors Effected

Abstract: Hydraulic valve system is a complex system with multiple characteristics affected by multiple geometric elements It will be essentially important to the manufacture process to establish the prediction model of the system characteristics by using the geometric elements and achieve the goal of prediction On the basis of synthesizing the features of the back propagation (BP) neural network and RBF neural network,a prediction model which is a new hybrid neural network based on the BP neural network and radial basis function (RBF) neural network is presented And the hybrid neural network is trained by using data measured from actual production The calculation results show that the hybrid neural network prediction model can improve the prediction accuracy of a single neural network model,and reach an average relative error of 0046 1 Therefore the proposed hybrid neural network can well satisfy the requirement of predicting the hydraulic valve characteristics

Study on the Railway Freight Volume Prediction Based on Hybrid Neural Network

Abstract: In order to achieve a more accurate prediction of railway freight volume,the paper establishes a model for it based on hybrid neural network technologyOn the basis on the analysis of past freight volumes,it ascertains the characteristic parameters of the network,decides upon the number of neurons on the input layer and the hidden layer in accordance with the reality of practice,thus the structure d the model,and finally establishes a hybrid neural network model for the prediction of railway freight volume

A Comparison of Hybrid Neural Network Based Breast Cancer Diagnosis Systems

Abstract: Breast cancer is the second leading cause of death among the women aged between 40 and 59 in the world. The diagnosis of such disease has been a challenging research problem. With the advancement of artificial intelligence in medical science, numerous AI based breast cancer diagnosis system have been proposed. Many researches combine different algorithms to develop hybrid systems to improve the diagnosis accuracy. In this study, we propose three artificial neural network based hybrid diagnosis systems respectively combining association rule, correlation and genetic algorithm. The effectiveness of these systems is examined on Wisconsin Breast Cancer Dataset. We then compare the accuracy of these three hybrid diagnosis systems. The results indicated that the neural network combining with association rule not only has excellent dimensionality reduction ability but also has the similar accurate prediction with correlation based neural network which has best accurate prediction rate among all three systems compared.

CNN Real-Time Detection of Vandalism Using a Hybrid -LSTM Deep Learning Neural Networks.

Abstract: Vandalism is a deliberate damage to property by humans and it has become rampant in the engineering fields. The activity results into huge financial and social loses and the vice is declared when human image is detected in the restricted area without authority to cause an unauthorized change in a predetermined scene that could be vandalized. This act requires an automated real-time detection of the presence of the vandal so that he can be stopped from damaging the property. Human Image recognition process is the best method for detection of vandalism. In this research paper, we propose a deep learning architecture combining Convolutional Neural Networks and Long Short Term memory (CNN-LSTM) which has the ability to exhaust spatial relationship and temporal prediction of the output. The main objective of this research work is to develop, train, test and validate CNN-LSTM against CNN and LSTM models to prove the superiority of the proposed model in image recognition. Image detection is achieved by feeding the images captured by installed image sensors (CCD camera) to a hybrid neural network classifier which is trained to recognize human images. The CNN-LSTM hybrid approach not only improves the predictive accuracy of image recognition from raw data but also reduces the computational complexity. The model is trained and tested with image-Net dataset which is the largest clean image dataset for vision research. Results show that the proposed model is able to achieve a training accuracy of 98% while a standalone CNN achieved 88%. The result show that the hybrid model is superior.