Regularization theory in the study of generalization ability of a biological neural network model

Biological neural network system is a complex nonlinear dynamic system, and research on its dynamics is an important topic at home and abroad. This paper briefly introduces the dynamic characteristics and influencing factors of the neural network system, including the effects of time delay and noise on neural network synchronization, synchronous transition, and stochastic resonance, and introduces the modeling of the neural network system. There are irregular mixing problems in the complex biological neural network system. The BP neural network algorithm can be used to solve more complex dynamic behaviors and can optimize the global search. In order to ensure that the neural network increases the biological characteristics, this paper adjusts the parameters of the BP neural network to receive EEG signals in different states. It can simulate different frequencies and types of brain waves, and it can also carry out a variety of simulations during the operation of the system. Finally, the experimental analysis shows that the complex biological neural network model proposed in this paper has good dynamic characteristics, and the application of this algorithm to data information processing, data encryption, and many other aspects has a bright prospect.

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Novel Cooperative Multi-Input Multilayer Perceptron Neural Network Performance Analysis with Application of Solar Irradiance Forecasting

International Journal of Photoenergy ◽

10.1155/2021/7238293 ◽

2021 ◽

Vol 2021 ◽

pp. 1-24

Author(s):

M. Madhiarasan ◽

Mohamed Louzazni ◽

Partha Pratim Roy

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Multilayer Perceptron ◽

Solar Irradiance ◽

Meteorological Parameters ◽

Weather Condition ◽

Forecasting Model ◽

Generalization Ability ◽

Forecasting Models

To forecast solar irradiance with higher accuracy and generalization capability is challenging in the photovoltaic (PV) energy system. Meteorological parameters are highly influential in solar irradiance, leading to intermittent and randomicity. Forecasting using a single neural network model does not have sufficient generalization ability to achieve the optimal forecasting of solar irradiance. This paper proposes a novel cooperative multi-input multilayer perceptron neural network (CMMLPNN) to mitigate the issues related to generalization and meteorological effects. Authors develop a proposed forecasting neural network model based on the amalgamation of two inputs, three inputs, four inputs, five inputs, and six inputs associated multilayer perceptron neural network. In the proposed forecasting model (CMMLPNN), the authors overcome the variance based on the meteorological parameters. The amalgamation of five multi-input multilayer perceptron neural networks leads to better generalization ability. Some individual multilayer perceptron neural network-based forecasting models outperform in some situations, but cannot assure generalization ability and suffer from the meteorological weather condition. The proposed CMMLPNN (cooperative multi-input multilayer perceptron neural network) achieves better forecasting accuracy with the generalization ability. Therefore, the proposed forecasting model is superior to other neural network-based forecasting models and existing models.

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Research on the GPS Elevation Fitting with RBF Neural Network Model Considering Effects of Sample Data Preprocessing

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.568-570.817 ◽

2014 ◽

Vol 568-570 ◽

pp. 817-821 ◽

Cited By ~ 1

Author(s):

Li Long Liu ◽

Jun Yu Li ◽

Chen Hui Cai ◽

Guo Biao Lin

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Rbf Neural Network ◽

Data Preprocessing ◽

Generalization Ability ◽

Preprocessing Method ◽

Sample Data

RBF neural network and three kinds of preprocessing methods are introduced, and this paper used these preprocessing methods combined with RBF neural network and strict RBF neural network to perform elevation fitting. Comparing and analyzing the fitting results, the results show that preprocessing methods can affect elevation fitting results. Centralized preprocessing data maximum improves RBF neural network elevation fitting precision, and it also let RBF neural network have stronger generalization ability. Normalization preprocessing methods are not necessarily optimal. It is essential for us to choose preprocessing method to fit the elevation.

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