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.

Modeling of supercritical ethane extraction by artificial neural networks

Abstract: In this paper, an artificial neural network that considers the system as a black box is designed for the mass transfer modeling of supercritical ethane extraction In addition, a hybrid model using both neural network and Peng-Robinson state equation is developed for supercritical ethane extraction, where the neural network is used to generate the nonlinear binary interaction parameter of the Peng-Robinson state equation The predictions of the proposed neural network models are compared to a conventional model with a Peng-Robinson equation of state in literature Generally, the results using the proposed models are better than those using the conventional model

CNN-DNN hybrid neural network based noise reduction method

Abstract: The invention provides a CNN-DNN hybrid neural network based noise reduction method. The method is implemented by the following steps that 1, a CNN-DNN hybrid neural network noise reduction model is established; 2, a training set is established for training the CNN-DNN hybrid neural network noise reduction model established in the step 1; and 3, a speech signal needing to be subjected to noise reduction is input to the trained CNN-DNN hybrid neural network noise reduction model in the step 3, and a clean speech signal spectrum is output. The CNN-DNN hybrid neural network based noise reductionmethod has better automatic identification separation and removal capabilities on transient noise and non-transient noise.

Навчання модулів нейронної мережі

Abstract: Currently, there exists a huge number of neural networks of different classes, each with itsown advantages and disadvantage. However, there aren’t a lot of focus on hybrid neural networks, basedon the combination of knowт topologies of neural networks. Modular organization principle seems to bevery promising, however principles of its module creation isn’t known and needs further research. Thepresent study, therefore, proposes some methods of hybrid neural network module creation and theirlearning algorithms

A hybrid neural network based speech recognition system for pervasive environments

Abstract: One of the major drawbacks to using speech as the input to any pervasive environment is the requirement to balance accuracy with the high processing overheads involved. This paper presents an Arabic speech recognition system (called UbiqRec), which address this issue by providing a natural and intuitive way of communicating within ubiquitous environments, while balancing processing time, memory and recognition accuracy. A hybrid approach has been used which incorporates spectrographic information, singular value decomposition, concurrent self-organizing maps (CSOM) and pitch contours for Arabic phoneme recognition. The approach employs separate self-organizing maps (SOM) for each Arabic phoneme joined in parallel to form a CSOM. The performance results confirm that with suitable preprocessing of data, including extraction of distinct power spectral densities (PSD) and singular value decomposition, the training time for CSOM was reduced by 89%. The empirical results also proved that overall recognition accuracy did not fall below 91%.

A hybrid neural network based vision-guided robotic system

Abstract: There are two primary methods for mapping an input image to robot motion: computed kinematics and visual servoing. Computed kinematics uses a kinematic transform between the image plane and the world frame. Computed kinematics algorithms require only a single iteration, but are sensitive to calibration errors. Visual servoing uses a control law to regulate the image to a desired state. Visual servoing is more robust, but requires more computation to reach a solution. To balance these opposing factors, we proposed a hybrid system that uses an initial computed kinematics move followed by a visual servoing correction, thereby providing a compromise between speed and accuracy. A linear approximation model and a neural network were used to approximate the kinematic transform between the image and world frames. A PD control system is used to regulate the image to its final state.