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.

Dynamic On‐Line Reoptimization Control of a Batch MMA Polymerization Reactor Using Hybrid Neural Network Models

Abstract: A hybrid neural network model based on-line reoptimization control strategy is developed for a batch polymerization reactor. To address the difficulties in batch polymerization reactor modeling, the hybrid neural network model contains a simplified mechanistic model covering material balance assuming perfect temperature control, and recurrent neural networks modeling the residuals of the simplified mechanistic model due to imperfect temperature control. This hybrid neural network model is used to calculate the optimal control policy. A difficulty in the optimal control of batch polymerization reactors is that the optimization effort can be seriously hampered by unknown disturbances such as reactive impurities and reactor fouling. With the presence of an unknown amount of reactive impurities, the off-line calculated optimal control profile will be no longer optimal. To address this issue, a strategy combining on-line reactive impurity estimation and on-line reoptimization is proposed in this paper. The amount of reactive impurities is estimated on-line during the early stage of a batch by using a neural network based inverse model. Based on the estimated amount of reactive impurities, on-line reoptimization is then applied to calculate the optimal reactor temperature profile for the remaining time period of the batch reactor operation. This approach is illustrated on the optimization control of a simulated batch methyl methacrylate polymerization process.

Hybrid neural network based on novel audio feature for vehicle type identification.

Abstract: Due to the audio information of different types of vehicle models are distinct, the vehicle information can be identified by the audio signal of vehicle accurately. In real life, in order to determine the type of vehicle, we do not need to obtain the visual information of vehicles and just need to obtain the audio information. In this paper, we extract and stitching different features from different aspects: Mel frequency cepstrum coefficients in perceptual characteristics, pitch class profile in psychoacoustic characteristics and short-term energy in acoustic characteristics. In addition, we improve the neural networks classifier by fusing the LSTM unit into the convolutional neural networks. At last, we put the novel feature to the hybrid neural networks to recognize different vehicles. The results suggest the novel feature we proposed in this paper can increase the recognition rate by 7%; destroying the training data randomly by superimposing different kinds of noise can improve the anti-noise ability in our identification system; and LSTM has great advantages in modeling time series, adding LSTM to the networks can improve the recognition rate of 3.39%.

Segmentation of MR and CT Images Using a Hybrid Neural Network Trained by Genetic Algorithms

Abstract: A novel hybrid neural network trained by the genetic algorithms is presented. Genetic algorithms are used to improve the neural net's classification performance while minimizing the number of nodes. Each node of the network forms a closed region in the input space. The closed regions, which are formed by the nodes, intersect each other. The performance of the proposed hybrid neural network is compared with the multilayer perceptron, and the restricted Coulomb energy network for the segmentation of MR and CT head images. Experimental results show that the proposed neural network gives the best classification performance with a small number of nodes in short training times.

Video human body attribute identification method based on deep adversarial network

Abstract: The invention provides a deep architecture for detecting attributes (such as gender, race and clothing) of a human body in a monitoring video. The method is characterized by, through capability of a hybrid neural network and a part-based method, carrying out decomposition forecasting on an object in an image, and giving robustness; carrying out training based on weighted loss, obtaining attributeprediction scores, re-constructing a network to enable the architecture to have robustness to occlusion, eliminating obstacles, and carrying out classification on occlusion images through a discriminator network; and finally, improving resolution of the images through a super-resolution network, and obtaining attribute identification result of the video human body. The method can improve the resolution of the low-resolution images, process occlusion problems, and can effectively extract attributes even under conditions of poor resolution and strong occlusion, so that identification efficiencyis improved greatly; and the method is suitable for a plurality of application fields.

Use of Neural Networks in Forecasting Financial Market

Abstract: In today's volatile financial market the demand for an accurate option price forecaster has been a focal point for researchers. The purpose of this study is to forecast option prices using neural networks. Initially simple neural network was implemented using twenty year period data from S&P 500 index call option prices. The prediction result was better than that of traditional Black-Scholes model. A hybrid neural network was developed that utilized aspects of Black- Scholes model into the neural network and tested against the traditional approach and simple neural network. The hybrid neural network outperformed performance of the tradition forecasting model and improved prediction results of simple neural networks.