Common nature of learning between BP-type and Hopfield-type neural networks

Guo, Dongsheng; Zhang, Yunong; Zhang, Xiao; Mao, Mingzhi; Liu, Jianxi

doi:10.1016/j.neucom.2015.04.032

Cited by 28 publications

(6 citation statements)

References 42 publications

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“…At present, the most popularly used ANN is the BP neural network 25 , which is composed of the input layer, hidden layer, and output layer 26 (Wang et al ., 2017). It has been widely applied to functional approximation, pattern recognition, classification, and data compression 27 ; however, its application for the predication and optimization of drug development, especially for multicomponent herbal medicine, is limited.…”

Section: Methodsmentioning

confidence: 99%

Novel application of neural network modelling for multicomponent herbal medicine optimization

Ren

Lei

Deng

et al. 2019

Sci Rep

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The conventional method for effective or toxic chemical substance identification of multicomponent herbal medicine is based on single component separation, which is time-consuming, labor intensive, inefficient, and neglects the interaction and integrity among the components; therefore, it is necessary to find an alternative routine to evaluate the components more efficiently and scientifically. In this study, sodium aescinate injection (SAI), obtained from different manufacturers and prepared as “components knockout” samples, was chosen as the case study. The chemical fingerprints of SAI were obtained by high-performance liquid chromatography to provide the chemical information. The effectiveness and irritation of each sample were evaluated using anti-inflammatory and irritation tests, and then “Gray correlation” analysis (GCA) was applied to rank the effectiveness and irritability of each component to provide a preliminary judgment for product optimization. The prediction model of the proportions of the expected components was constructed using the artificial neural network. The results of the GCA showed that the irritation sorting of each SAI component was in the order of B > A > G > J > I > H > D > F > E > C and the effectiveness sorting of SAI components was in the order of D > C > B > A > F > E > H > I > G > J; the predictive proportion of SAI was optimized by the BP neural network as A: B: C: D: E: F = 0.7526: 0.5005: 5.4565: 1.4149: 0.8113: 1.0642. This study provided a scientific, accurate, reliable, and efficient approach for the proportion optimization of multicomponent drugs, which has a good prospect of popularization and application in product upgrading and development of herbal medicine.

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Section: Methodsmentioning

confidence: 99%

Novel application of neural network modelling for multicomponent herbal medicine optimization

Ren

Lei

Deng

et al. 2019

Sci Rep

View full text Add to dashboard Cite

show abstract

“…(7) and (8), where the learning rate η is expressed. This process is repeated (4) By changing the network parameters, such as the number of neurons, the number of hidden layers, and the learning algorithm, the BP neural network model can obtain the network model that is most suitable for solving the problem [33]. To predict vehicle speed, it is necessary to adjust the input neuron types of the neural network model according to the range of vehicle speed fluctuation and the structure of the prediction model [34].…”

Section: Bp Neural Network Prediction Algorithmmentioning

confidence: 99%

Combined Prediction for Vehicle Speed with Fixed Route

Zhang

Liu

2020

Chin. J. Mech. Eng.

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Achieving accurate speed prediction provides the most critical support parameter for high-level energy management of plug-in hybrid electric vehicles. Nowadays, people often drive a vehicle on fixed routes in their daily travels and accurate speed predictions of these routes are possible with random prediction and machine learning, but the prediction accuracy still needs to be improved. The prediction accuracy of traditional prediction algorithms is difficult to further improve after reaching a certain accuracy; problems, such as over fitting, occur in the process of improving prediction accuracy. The combined prediction model proposed in this paper can abandon the transitional dependence on a single prediction. By combining the two prediction algorithms, the fusion of prediction performance is achieved, the limit of the single prediction performance is crossed, and the goal of improving vehicle speed prediction performance is achieved. In this paper, an extraction method suitable for fixed route vehicle speed is designed. The application of Markov and back propagation (BP) neural network in predictions is introduced. Three new combined prediction methods, all named Markov and BP Neural Network (MBNN) combined prediction algorithm, are proposed, which make full use of the advantages of Markov and BP neural network algorithms. Finally, the comparison among the prediction methods has been carried out. The results show that the three MBNN models have improved by about 19%, 28%, and 29% compared with the Markov prediction model, which has better performance in the single prediction models. Overall, the MBNN combined prediction models can improve the prediction accuracy by 25.3% on average, which provides important support for the possible optimization of plug-in hybrid electric vehicle energy consumption.

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“…Aiming at the shortcomings [6] of general neural network convergence slow, large sample size demand, and easy to fall into local optimum, Huang [7] proposed a new algorithm for single hidden layer feedforward neural network -Extreme Learning Machine (ELM). The only optimal solution can be obtained by setting the number of hidden layer neurons.…”

Section: Ga-elm Neural Networkmentioning

confidence: 99%

Kansei Image Evaluation of Teacup based on GA-ELM

Jiang¹,

Li²

2018

2018 3rd International Conference on Mechatronics and Information Technology (ICMIT 2018)

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In order to improve the design efficiency of cultural product , the Genetic Algorithm-Extreme Learning Machine (GA-ELM) network model is used to evaluate the Kansei image of cultural product. Firstly, the design variables of teacup are analyzed, and then 27 kinds of 3D experimental samples are constructed based on the orthogonal experimental results. Semantic Difference Method (SD method) is used to quantify the samples Kansei image score. The Kansei image evaluation model of GA-ELM cultural products is trained with the design variables of teacup as input parameter and the Kansei image score as output parameter. The reliability of the model is demonstrated by case design. The results show that using GA-ELM model to predict the Kansei evaluation of cultural products has higher feasibility and reliability, can effectively improve the emotional design efficiency of cultural products, and provide direction guidance for design practice.

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Common nature of learning between BP-type and Hopfield-type neural networks

Cited by 28 publications

References 42 publications

Novel application of neural network modelling for multicomponent herbal medicine optimization

Novel application of neural network modelling for multicomponent herbal medicine optimization

Combined Prediction for Vehicle Speed with Fixed Route

Kansei Image Evaluation of Teacup based on GA-ELM

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