Topic
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.
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10 Jul 1999
TL;DR: The approach of combining advanced neural networks and conventional error correction is promising for improved ITS applications for improving demand prediction and traffic data modeling to support pro-active control.
Abstract: Many operating agencies are currently developing computerized freeway traffic management systems to support traffic operations as part of the intelligent transportation system (ITS) user service improvements. This study illustrates the importance of using simplified data analysis and presents a promising approach for improving demand prediction and traffic data modeling to support pro-active control. This study found that the approach of combining advanced neural networks and conventional error correction is promising for improved ITS applications.
2 citations
21 Jun 2011
TL;DR: The simulation result indicates that the hybrid network model and model-based multiobjective optimal algorithm are effective in polymerizing of PET with maximum yield and the best quality.
Abstract: A multiobjective intelligence optimal approach in polymerizing of PET with maximum yield and the best quality is proposed. The hybrid neural network based on B-spline and diagonal recursive neural network is used to model the PET process qualities, i.e. the Intrinsic Viscosity and Molecular Weight distribution. Then a hybrid NSGAII-PSO optimal algorithm with penalty functions is applied to solve the multiobjective optimal problem in order to get the best operation conditions. The simulation result indicates that the hybrid network model and model-based multiobjective optimal algorithm are effective.
2 citations
TL;DR: A robust short term load forecasting technique for one to seven days ahead is introduced based on particle swarm optimization (PSO) and Levenberg Marquardt (LM) neural network forecast model, where the PSO and LM algorithm are used for the training process of neural network.
Abstract: One of the important factors in generating low cost electrical power is the accurate forecasting of electricity consumption called load forecasting. The major objective of the load forecasting is to trim down the error between actual load and forecasted load. Due to the nonlinear nature of load forecasting and its dependency on multiple variables, the traditional forecasting methods are normally outperformed by artificial intelligence techniques. In this research paper, a robust short term load forecasting technique for one to seven days ahead is introduced based on particle swarm optimization (PSO) and Levenberg Marquardt (LM) neural network forecast model, where the PSO and LM algorithm are used for the training process of neural network. The proposed methods are tested to predict the load of the New England Power Pool region's grid and compared with the existing techniques using mean absolute percentage errors to analyze the performance of the proposed methods. Forecast results confirm that the proposed LM and PSO-based neural network schemes outperformed the existing techniques.
2 citations
Patent•
26 Jul 2019
TL;DR: In this article, a hybrid neural network and a project recommendation model was proposed to improve the performance of the recommendation system and improve the recommendation effect. But the authors did not reveal the design of the model.
Abstract: The invention discloses a construction method and device of a project recommendation model based on a hybrid neural network and a project recommendation method. The construction method comprises the following steps: filtering comment information, preprocessing the filtered comment information, and learning context features related to a project in the preprocessed comment information and user features and project features in scoring information by using a convolutional neural network; subsequently, fusing and interacting the project characteristics in the user-project scoring information and the context characteristics in the comment information, integrating the learned user characteristics and the fused project characteristics into a multi-task learning framework, and performing joint training to obtain a project recommendation model based on the hybrid neural network. According to the invention, the two heterogeneous data of the scoring information and the comment information are integrated into one unified model, so that the implicit feature vectors of the user and the project can be learned more accurately, and the purposes of improving the performance of the recommendation system and improving the recommendation effect are achieved.
2 citations
27 Dec 2005
TL;DR: The stability and the bifurcation of the neuron cluster and factors affecting cluster generation are investigated.
Abstract: In this paper, dynamics of VSF-network (vibration synchronizing function network) is investigated. VSF-network is a model of neural networks that segments information from external world and fixes the segmented information. VSF-network is a hybrid neural network, and the chaos neuron is used for the hidden layer of it. VSF-network articulates information with the neuron cluster generated by the synchronizing vibration that the chaos neuron in hidden layer shows. We analyze the dynamics of generating the neuron cluster. Factors affecting cluster generation are investigated. The stability and the bifurcation of the neuron cluster and factors affecting cluster generation are investigated.
2 citations