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 networks as prediction models

Abstract: The paper presents hybrid neural networks as prediction models for water intake in water supply system. Previous research concerned establishing prediction models in the form of single neural networks: linear network (L), multi-layer network with error back propagation (MLP) and Radial Basis Function network (RBF). Currently, the models in the form of hybrid neural networks (L-MLP, L-RBF, MLP-RBF and L-MLP-RBF) were created. The prediction models were compared for obtaining optimal prognosis. Prediction models were done for working days, Saturdays and Sundays. The research was done for selected nodes of water supply system: detached house node and nodes for 4 hydrophore stations from different pressure areas of water supply system. Models for Sundays were presented in detail.

Fuzzy ART K-Means Clustering Technique: a hybrid neural network approach to cellularmanufacturing systems

Abstract: Cellular manufacturing system (CMS) is regarded as an efficient production strategy for batch type of production. Literature suggests, since the last two decades neural network has been intensively used in cell formation while production factor such as operation time is merely considered. This paper presents a new hybrid neural network approach, Fuzzy ART K-Means Clustering Technique (FAKMCT), to solve the part machine grouping problem in CMS considering operation time. The performance of the proposed technique is tested with problems from open literature and the results are compared to the existing clustering models such as simple K-means algorithm and modified ART1 algorithm as found in the recent literature. The results support the better performance of the proposed algorithm. The novelty of this study lies in the simple and efficient methodology to produce quick solutions with least computational efforts.

A hybrid neural network for large-scale expressway network OD prediction based on toll data.

Abstract: Accurate Origin-Destination (OD) prediction is significant for effective traffic monitor, which can support operation decision in traffic planning and management field. The enclosed expressway network system like toll gates system in China can collect mounts of trip records which can be gathered for OD prediction. The paper develops a novel neural network, which is named Expressway OD Prediction Neural Network (EODPNN) for toll data-based prediction. The network consists of the following three modules: The Feature Extension Module, the Memory Module, and the Prediction Module. In the process, the attributes data which can reflect the city attribute such as GDP, population, and the number of vehicles are considered to embeded into the notwork to increase the accuracy of the model. For the applicability improvment of the model, we categorize the cities in multiple classes based on their economy and population scales in this paper, which can provide a higher accurate prediction of OD by EODPNN. The results shows that, comparing to the traditional model like ARIMA and SVM, or typical neural networks like Bidirectional Long Short-term Memory, the EODPNN delivers a better prediction performance. The method proposed in this paper has been fully verified and has a potential to transplant to the other OD data-based management systems for a more accurate and flexible prediction.

A Hybrid Intelligent Active Force Controller for Articulated Robot Arms Using Dynamic Structure Neural Network

Abstract: The key feature of this paper is the application of a robotic control concept – Active Force Control (AFC) In this type of control, the unknown friction effect of the robotic arm may be compensated by the AFC method AFC involves the direct measurement of the acceleration and force quantities and therefore, the process of estimating the system ‘disturbance’ due to friction becomes instantaneous and purely algebraic However, the AFC strategy is very practical provided a good estimation of the inertia matrix of articulated robot arm is acquired A dynamic structure neural network – Growing Multi-experts Network (GMN) is developed to estimate the robot inertia matrix The growing and pruning mechanism of GMN ensures the optimum size of the network that results in an excellent generalization capability of the network Active Force Control (AFC) in conjunction with GMN successfully reduces the velocity and position tracking errors in spite of robot joint friction The embedded GMN is capable of coupling the inertia matrix estimation on-line that clearly enhances the performance of AFC controller The robustness and effectiveness of the new hybrid neural network-based AFC scheme are demonstrated clearly with regard to two link articulated robot and a simulated two-degree of freedom Puma 560 robot