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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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Book ChapterDOI
19 Nov 2020
TL;DR: In this paper, a hybrid approach combining genetic programming and feed-forward Neural Networks (NNs) is proposed to improve the robustness of the GP control law against unforeseen environmental changes.
Abstract: The proposed work aims to introduce a novel approach to Intelligent Control (IC), based on the combined use of Genetic Programming (GP) and feedforward Neural Network (NN). Both techniques have been successfully used in the literature for regression and control applications, but, while a NN creates a black box model, GP allows for a greater interpretability of the created model, which is a key feature in control applications. The main idea behind the hybrid approach proposed in this paper is to combine the speed and flexibility of a NN with the interpretability of GP. Moreover, to improve the robustness of the GP control law against unforeseen environmental changes, a new selection and crossover mechanisms, called Inclusive Tournament and Inclusive Crossover, are also introduced. The proposed IC approach is tested on the guidance control of a space transportation system and results, showing the potentialities for real applications, are shown and discussed.

4 citations

Journal ArticleDOI
TL;DR: The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average which is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process.
Abstract: This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process.

4 citations

Book ChapterDOI
25 Jun 2014
TL;DR: The results of the experiments show that using automatically estimated subclasses for HNNP delivers the best classification performance and outperforms also single state-of-the-art neural networks as well as ensemble methods.
Abstract: Although nowadays many artificial intelligence and especially machine learning research concerns big data, there are still a lot of real world problems for which only small and noisy data sets exist. Applying learning models to those data may not lead to desirable results. Hence, in a former work we proposed a hybrid neural network plait (HNNP) for improving the classification performance on those data. To address the high intraclass variance in the investigated data we used manually estimated subclasses for the HNNP approach. In this paper we investigate on the one hand the impact of using those subclasses instead of the main classes for HNNP and on the other hand an approach for an automatic subclasses estimation for HNNP to overcome the expensive and time consuming manual labeling. The results of the experiments with two different real data sets show that using automatically estimated subclasses for HNNP delivers the best classification performance and outperforms also single state-of-the-art neural networks as well as ensemble methods.

4 citations

Proceedings ArticleDOI
25 Jun 2001
TL;DR: On-line reactive impurity estimation is combined with batch reactor optimal control to form a novel re-optimisation control strategy and this approach is illustrated on the optimisation control of a simulated batch MMA polymerisation process.
Abstract: A hybrid recurrent neural network model based on-line re-optimisation control strategy is developed for batch polymerisation reactors. The hybrid model contains a simplified mechanistic model covering material balance and simplified reaction kinetics only and recurrent neural networks. Based on this hybrid neural network model, optimal control policy can be calculated. A difficulty in the optimal control of batch polymerisation reactors is that optimisation effort can be seriously hampered by unknown disturbances such as reactive impurities and reactor fouling. A technique for on-line estimation of reactive impurity and reactor fouling has been developed by Zhang et al. (1999). In this contribution, on-line reactive impurity estimation is combined with batch reactor optimal control to form a novel re-optimisation control strategy. When there exists an unknown amount of reactive impurities, the off-line calculated optimal control profile will be no longer optimal. On-line impurity estimation is applied to estimate the amount of reactive impurities during the early stage of the batch. Based on the estimated amount of reactive impurities, on-line re-optimisation is applied to calculate the optimal reactor temperature profile for the remaining time period of the batch reactor operation. This approach is illustrated on the optimisation control of a simulated batch MMA polymerisation process.

4 citations


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