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 Architecture for On-Line Learning

Abstract: Approaches to machine intelligence based on brain models have stressed the use of neural networks for generalization. Here we propose the use of a hybrid neural network architecture that uses two kind of neural networks simultaneously: (i) a surface learning agent that quickly adapt to new modes of operation; and, (ii) a deep learning agent that is very accurate within a specific regime of operation. The two networks of the hybrid architecture perform complementary functions that improve the overall performance. The performance of the hybrid architecture has been compared with that of back-propagation perceptrons and the CC and FC networks for chaotic time-series prediction, the CATS benchmark test, and smooth function approximation. It has been shown that the hybrid architecture provides a superior performance based on the RMS error criterion.

Genetic Algorithm optimized Neural Network based Adaptive ECG Interference Canceller for Premature Infants in Incubators

Abstract: This paper proposed a hybrid neural network Back propagation (BP) algorithm optimized by Genetic Algorithm (GA) for the diminution of the fundamental electromagnetic interferences in Incubators. Gradient based techniques have been proposed in the past for the elimination of incubator noise but they are susceptible to local minima problem. Genetic algorithms are a class of optimization procedure which is good at examining an intelligent way for selecting the number of hidden layer neurons, learning rate and momentum constant of the Artificial Neural Network (ANN) to find values close to the global minimum. The result analysis shows that the proposed approach shows good performance in cancelling the ECG interference over other conventional approaches.

Hybrid neural network techniques for storm system identification and tracking

Abstract: In this paper a hybrid neural network/genetic algorithm (NN/GA) approach is presented that analyzes the behavior of storm systems from one time frame to the next. The goal of the hybrid neural network algorithm is to improve the classifier output by reducing the number of infeasible solutions using constraint optimization techniques. The input to the hybrid neural network algorithm is the output from a traditional backpropagation neural network. The hybrid NN/GA analyzes the backpropagation neural network output for logical consistencies and makes changes to the classification results based on strength of neural network classifications and satisfaction of logical constraints. The results are compared with classification results obtained using the linear discriminant analysis, k-nearest neighbor rule, and backpropagation neural network techniques.

Characterization and recognition of handwritten digits using Julia.

Abstract: Automatic image and digit recognition is a computationally challenging task for image processing and pattern recognition, requiring an adequate appreciation of the syntactic and semantic importance of the image for the identification ofthe handwritten digits. Image and Pattern Recognition has been identified as one of the driving forces in the research areas because of its shifting of different types of applications, such as safety frameworks, clinical frameworks, diversion, and so this http URL this study, for recognition, we implemented a hybrid neural network model that is capable of recognizing the digit of MNISTdataset and achieved a remarkable result. The proposed neural model network can extract features from the image and recognize the features in the layer by layer. To expand, it is so important for the neural network to recognize how the proposed modelcan work in each layer, how it can generate output, and so on. Besides, it also can recognize the auto-encoding system and the variational auto-encoding system of the MNIST dataset. This study will explore those issues that are discussed above, and the explanation for them, and how this phenomenon can be overcome.

Application method of college student portrait technology based on hybrid neural network

Abstract: The invention discloses an application method of a college student portrait technology based on a hybrid neural network. The application method comprises the following steps of S1, performing label design on student portraits based on high-dimensional clustering; S2, establishing a student portrait classification model based on the hybrid neural network; S3, obtaining an optimal solution by utilizing a combination result, and further obtaining a student portrait; obtaining a merging result by utilizing the acquired data and utilizing a hybrid neural network model of a convolutional neural network and a feedforward neural network, and performing optimal solution calculation on a hybrid neural network framework by utilizing Bayesian personalized sorting and a genetic algorithm by utilizing the merging result to obtain student portraits. The beneficial effect of the application method is that the application method assists students in academic development.