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.

A semi-parametric hybrid neural model for nonlinear blind signal separation.

Abstract: Nonlinear blind signal separation is an important but rather difficult problem. Any general nonlinear independent component analysis algorithm for such a problem should specify which solution it tries to find. Several recent neural networks for separating the post nonlinear blind mixtures are limited to the diagonal nonlinearity, where there is no cross-channel nonlinearity. In this paper, a new semi-parametric hybrid neural network is proposed to separate the post nonlinearly mixed blind signals where cross-channel disturbance is included. This hybrid network consists of two cascading modules, which are a neural nonlinear module for approximating the post nonlinearity and a linear module for separating the predicted linear blind mixtures. The nonlinear module is a semi-parametric expansion made up of two sub-networks, one of which is a linear model and the other of which is a three-layer perceptron. These two sub-networks together produce a "weak" nonlinear operator and can approach relatively strong nonlinearity by tuning parameters. A batch learning algorithm based on the entropy maximization and the gradient descent method is deduced. This model is successfully applied to a blind signal separation problem with two sources. Our simulation results indicate that this hybrid model can effectively approach the cross-channel post nonlinearity and achieve a good visual quality as well as a high signal-to-noise ratio in some cases.

Hybrid neural network design and implementation on FPGA for infant cry recognition

Abstract: It has been found that the infant's crying has much information on its sound wave. For small infants crying is a form of communication, a very limited one, but similar to the way adults communicate. In this work we present the design of an Automatic Infant Cry Recognizer hybrid system, that classifies different kinds of cries, with the objective of identifying some pathologies in recently born babies. The system is based on the implementation of a Fuzzy Relational Neural Network (FRNN) model on a standard reconfigurable hardware like Field Programmable Gate Arrays (FPGAs). To perform the experiments, a set of crying samples is divided in two parts; the first one is used for training and the other one for testing. The input features are represented by fuzzy membership functions and the links between nodes, instead of regular weights, are represented by fuzzy relations. The training adjusts the relational weight matrix, and once its values have been adapted, the matrix is fixed into the FPGA. The goal of this research is to prove the performance of the FRNN in a development board; in this case we used the RC100 from Celoxica. The implementation process, as well as some results is shown.

A neural network expert system shell

Abstract: Presents the architecture of a hybrid neural network expert system shell. The system, structured around the concept of a "network element", is aimed at preserving the semantic structure of the expert system rules whilst incorporating the learning capability of neural networks into the inferencing mechanism. Using this architecture, every rule of the knowledge base is represented by a one- or two-layer neural network element. These network elements are dynamically linked up to form a rule-tree during the inferencing process. The system is also able to adjust its inferencing strategy according to different users and situations. A rule editor is also provided to enable easy maintenance of the neural network rule elements. >

Abnormal flow detection method and system based on hybrid neural network

Abstract: The invention relates to an abnormal flow detection method and system based on a hybrid neural network, and the method comprises the steps: firstly, collecting network flow data, and carrying out thefeature extraction and data preprocessing through taking network flow as granularity; learning spatial features in the network traffic data through a convolutional neural network; inputting the features containing the spatial information into a bidirectional long-short time memory network to further learn the time sequence features of the bidirectional long-short time memory network; finally, outputting a detection result. Compared with an existing machine learning and deep learning abnormal flow detection method, the method has the advantages that high-dimensional features can be better mined, and the accuracy of an intrusion detection model is improved. The method is reasonable in design, and the accuracy rate, the detection rate and the accuracy rate of the obtained classification modelare all high.

CBN tool flank wear modelling using Hybrid Neural Network

Abstract: Accurate tool wear modelling is indispensable for successful hard turning technology implementation. In this study, a Hybrid Neural Network-based modelling approach, which integrates an analytical tool wear model and an artificial neural network, is proposed to predict Cubic Boron Nitride (CBN) tool flank wear in turning hardened 52100 bearing steel. Extended Kalman Filter algorithm is used to train the proposed neural network, and the network connectivity is further optimised to achieve an improved and robust modelling performance. Results show that the proposed Hybrid Neural Network excels the analytical tool wear model approach and the general neural network-based modelling approach.