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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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Proceedings ArticleDOI
01 Jan 2009
TL;DR: In this work, a well understood and easy excess Rule of Five + Veber filter properties are selected as the topological descriptor to assure that the intelligent prediction system could be used widely, beneficial and more advantageous regardless at all user level within a drug discovery organization.
Abstract: An intelligent prediction system has been developed to discriminate drug-like and non drug-like molecules pattern. The system is constructed by using the application of advanced version of standard multilayer perceptron (MLP) neural network called Hybrid Multilayer Perceptron (HMLP) neural network and trained using Modified Recursive Prediction Error (MRPE) training algorithm. In this work, a well understood and easy excess Rule of Five + Veber filter properties are selected as the topological descriptor. The main idea behind the selection of this simple descriptor is to assure that the system could be used widely, beneficial and more advantageous regardless at all user level within a drug discovery organization.

1 citations

Journal ArticleDOI
30 Jun 2019
TL;DR: This study proposes an alternatives procedure using a dual response approach and artificial intelligence to improve process capability and compares it with conventional optimization models to show the improvement in procedures.
Abstract: Process capability has long been recognized as an important performance measure to prove how well the process meets the requirements. Process capability can be improved by applying dual response approach, to determine optimal input factors. Using of artificial intelligence can optimize the prediction of the best input combination with a limited number of experiments. This study proposes an alternatives procedure using a dual response approach and artificial intelligence. One of the most common robust design models has been formulated to minimize variability while maintaining the mean on the desired target. A study case was selected to implement the proposed approach and compare it with conventional optimization models to show the improvement in procedures.

1 citations

Journal Article
TL;DR: A hybrid IDS system integrating BP network, genetic algorithms and Snort is proposed, which integrates the advantages of abnormal and misuse detection, and overcomes the disadvantages of single detection mode.
Abstract: Aiming at that the intrusion detection system mostly takes single detection mode,which is difficult to solve the problem,of miss alarm,false alarm,and indistinguishability of unknown attacks,the network flow of different type is analyzed,a hybrid IDS system integrating BP network,genetic algorithms and Snort is proposedThis system integrates the advantages of abnormal and misuse detection,overcomes the disadvantages of single detection modeThe experiment results prove that this method could efficiently improve the detection rate and correctness rate

1 citations

Patent
12 Apr 2019
TL;DR: Wang et al. as mentioned in this paper proposed a crack identification method and system based on an ABC-BP neural network, which consists of extracting a feature vector of a pavement crack image, and further comprising the following steps: training a test sample by using the optimized BP neural network; and adopting the cross entropy of the predicted value and the true value of the test sample as an objective function of the vector characteristic to complete the recognition of the pavement crack.
Abstract: The invention provides a crack identification method and system based on an ABC-BP neural network. The method comprises the following steps: extracting a feature vector of a pavement crack image, andfurther comprising the following steps: training a test sample by using the optimized BP neural network; and adopting the cross entropy of the predicted value and the true value of the test sample asan objective function of the vector characteristic to complete the recognition of the pavement crack image. The invention provides a crack identification method and system based on an ABC-BP neural network. An adaptive factor is added to improve the search position and probability selection of an artificial bee colony (ABC) algorithm; an improved ABC algorithm is used for optimizing the weight andthe threshold value of the BP neural network, an improved ABC-BP hybrid neural network pavement crack recognition algorithm is established, and it is verified through experiments that the algorithm has good universality and effectiveness.

1 citations

Journal ArticleDOI
TL;DR: This paper proposes a new Initial CCT (Critical Clearing Time) estimation method using a hybrid neural network composed of iRprop (Improving the Resilient back PROPation Algorithm) and RAN (Resource Allocation Network).
Abstract: This paper proposes a new Initial CCT (Critical Clearing Time) estimation method using a hybrid neural network composed of iRprop (Improving the Resilient back PROPation Algorithm) and RAN (Resource Allocation Network). In transient stability study, CCT evaluation is very important but time consuming due to the fact it needs many iteration of time domain simulations gradually increasing the fault clearing time. The key to reduce the required computing time in this process is to find accurate initial estimation of CCT by a certain handy method before going to the iterative stage. As one of the strongest candidates of this handy method is the utilization of the pattern recognition ability of neural networks, which enable us to jump to a close estimation of the real CCT without any heavy computing burden. This paper proposes a new hybrid neural network which is a combination of the well-known iRprop and RAN. In the proposed method, the outputs of the hidden units of RAN are modified by multiplying the contribution factors calculated by an additional iRprop network. Numerical studies are done using two different test systems for the purpose of confirming the validity of the proposal. The result of the proposed method is the best. Properly evaluating the contribution of each input to the hidden units, the estimation error obtained by the proposed method is improved further than the original RAN based estimation.

1 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20233
20228
2021128
2020119
2019104
201863